On grounds of the advent of real-time applications,like autonomous driving,visual surveillance,and sports analysis,there is an augmenting focus of attention towards Multiple-Object Tracking(MOT).The tracking-by-detect...On grounds of the advent of real-time applications,like autonomous driving,visual surveillance,and sports analysis,there is an augmenting focus of attention towards Multiple-Object Tracking(MOT).The tracking-by-detection paradigm,a commonly utilized approach,connects the existing recognition hypotheses to the formerly assessed object trajectories by comparing the simila-rities of the appearance or the motion between them.For an efficient detection and tracking of the numerous objects in a complex environment,a Pearson Simi-larity-centred Kuhn-Munkres(PS-KM)algorithm was proposed in the present study.In this light,the input videos were,initially,gathered from the MOT dataset and converted into frames.The background subtraction occurred whichfiltered the inappropriate data concerning the frames after the frame conversion stage.Then,the extraction of features from the frames was executed.Afterwards,the higher dimensional features were transformed into lower-dimensional features,and feature reduction process was performed with the aid of Information Gain-centred Singular Value Decomposition(IG-SVD).Next,using the Modified Recurrent Neural Network(MRNN)method,classification was executed which identified the categories of the objects additionally.The PS-KM algorithm identi-fied that the recognized objects were tracked.Finally,the experimental outcomes exhibited that numerous targets were precisely tracked by the proposed system with 97%accuracy with a low false positive rate(FPR)of 2.3%.It was also proved that the present techniques viz.RNN,CNN,and KNN,were effective with regard to the existing models.展开更多
Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).How...Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.展开更多
As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to r...As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.展开更多
Unmanned aerial vehicles(UAVs)can be used to monitor traffic in a variety of settings,including security,traffic surveillance,and traffic control.Numerous academics have been drawn to this topic because of the challen...Unmanned aerial vehicles(UAVs)can be used to monitor traffic in a variety of settings,including security,traffic surveillance,and traffic control.Numerous academics have been drawn to this topic because of the challenges and the large variety of applications.This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it.It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile.The goal of this study is to develop a method that first extracts the region of interest(ROI),then finds and tracks the items of interest.The suggested system is divided into six stages.The photos from the obtained dataset are appropriately georeferenced to their actual locations in the first phase,after which they are all co-registered.The ROI,or road and its objects,are retrieved using the GrabCut method in the second phase.The third phase entails data preparation.The segmented images’noise is eliminated using Gaussian blur,after which the images are changed to grayscale and forwarded to the following stage for additional morphological procedures.The YOLOv3 algorithm is used in the fourth step to find any automobiles in the photos.Following that,the Kalman filter and centroid tracking are used to perform the tracking of the detected cars.The Lucas-Kanade method is then used to perform the trajectory analysis on the vehicles.The suggested model is put to the test and assessed using the Vehicle Aerial Imaging from Drone(VAID)dataset.For detection and tracking,the model was able to attain accuracy levels of 96.7%and 91.6%,respectively.展开更多
Pedestrian detection and tracking are vital elements of today’s surveillance systems,which make daily life safe for humans.Thus,human detection and visualization have become essential inventions in the field of compu...Pedestrian detection and tracking are vital elements of today’s surveillance systems,which make daily life safe for humans.Thus,human detection and visualization have become essential inventions in the field of computer vision.Hence,developing a surveillance system with multiple object recognition and tracking,especially in low light and night-time,is still challenging.Therefore,we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at night.In particular,we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared(IR)images using machine learning and tracking them using particle filters.Moreover,a random forest classifier is adopted for image segmentation to identify pedestrians in an image.The result of detection is investigated by particle filter to solve pedestrian tracking.Through the extensive experiment,our system shows 93%segmentation accuracy using a random forest algorithm that demonstrates high accuracy for background and roof classes.Moreover,the system achieved a detection accuracy of 90%usingmultiple templatematching techniques and 81%accuracy for pedestrian tracking.Furthermore,our system can identify that the detected object is a human.Hence,our system provided the best results compared to the state-ofart systems,which proves the effectiveness of the techniques used for image segmentation,classification,and tracking.The presented method is applicable for human detection/tracking,crowd analysis,and monitoring pedestrians in IR video surveillance.展开更多
Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that...Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so on.Besides,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety.The recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,etc.In this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian walkways.The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,etc.The proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition process.For object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized objects.For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)models.The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.展开更多
At present days,object detection and tracking concepts have gained more importance among researchers and business people.Presently,deep learning(DL)approaches have been used for object tracking as it increases the per...At present days,object detection and tracking concepts have gained more importance among researchers and business people.Presently,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking process.This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)model.The AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT model.The AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast R-CNN.The RPN is a full convolution network that concurrently predicts the bounding box and score of different objects.The RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection process.Besides,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature maps.The performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight decay.Finally,softmax layer is applied to classify the images.The performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes place.The outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.展开更多
Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating...Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.展开更多
High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy,high resolution and high efficiency.However,it...High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy,high resolution and high efficiency.However,it faces challenge in dense objects tracking and 3D trajectories reconstruction due to the characteristics of small size and dense distribution of fragment swarm.To address these challenges,this work presents a warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography.Firstly,background difference algorithm is utilized to extract the center and area of each fragment in the image sequence.Subsequently,a multi-object tracking(MOT)algorithm using Kalman filtering and Hungarian optimal assignment is developed to realize real-time and robust trajectories tracking of fragment swarm.To reconstruct 3D motion trajectories,a global stereo trajectories matching strategy is presented,which takes advantages of epipolar constraint and continuity constraint to correctly retrieve stereo correspondence followed by 3D trajectories refinement using polynomial fitting.Finally,the simulation and experimental results demonstrate that the proposed method can accurately track the motion trajectories and reconstruct the spatio-temporal distribution of 1.0×10^(3)fragments in a field of view(FOV)of 3.2 m×2.5 m,and the accuracy of the velocity estimation can achieve 98.6%.展开更多
Underwater monopulse space-time adaptive track-before-detect method,which combines space-time adaptive detector(STAD)and the track-before-detect algorithm based on dynamic programming(DP-TBD),denoted as STAD-DP-TBD,ca...Underwater monopulse space-time adaptive track-before-detect method,which combines space-time adaptive detector(STAD)and the track-before-detect algorithm based on dynamic programming(DP-TBD),denoted as STAD-DP-TBD,can effectively detect low-speed weak targets.However,due to the complexity and variability of the underwater environment,it is difficult to obtain sufficient secondary data,resulting in a serious decline in the detection and tracking performance,and leading to poor robustness of the algorithm.In this paper,based on the adaptive matched filter(AMF)test and the RAO test,underwater monopulse AMF-DP-TBD algorithm and RAO-DP-TBD algorithm which incorporate persymmetry and symmetric spectrum,denoted as PSAMF-DP-TBD and PS-RAO-DP-TBD,are proposed and compared with the AMF-DP-TBD algorithm and RAO-DP-TBD algorithm based on persymmetry array,denoted as P-AMF-DP-TBD and P-RAO-DP-TBD.The simulation results show that the four methods can work normally with sufficient secondary data and slightly insufficient secondary data,but when the secondary data is severely insufficient,the P-AMF-DP-TBD and P-RAO-DP-TBD algorithms has failed while the PSAMF-DP-TBD and PS-RAO-DP-TBD algorithms still have good detection and tracking capabilities.展开更多
Derailment of trains is not unusual all around the world,especially in developing countries,due to unidentified track or rolling stock faults that cause massive casualties each year.For this purpose,a proper condition...Derailment of trains is not unusual all around the world,especially in developing countries,due to unidentified track or rolling stock faults that cause massive casualties each year.For this purpose,a proper condition monitoring system is essential to avoid accidents and heavy losses.Generally,the detection and classification of railway track surface faults in real-time requires massive computational processing and memory resources and is prone to a noisy environment.Therefore,in this paper,we present the development of a novel embedded system prototype for condition monitoring of railway track.The proposed prototype system works in real-time by acquiring railway track surface images and performing two tasks a)detect deformation(i.e.,faults)like squats,shelling,and spalling using the contour feature algorithm and b)the vibration signature on that faulty spot by synchronizing acceleration and image data.A new illumination scheme is also proposed to avoid the sunlight reflection that badly affects the image acquisition process.The contour detection algorithm is applied here to detect the uneven shapes and discontinuities in the geometrical structure of the railway track surface,which ultimately detects unhealthy regions.It works by converting Red,Green,and Blue(RGB)images into binary images,which distinguishes the unhealthy regions by making them white color while the healthy regions in black color.We have used the multiprocessing technique to overcome the massive processing and memory issues.This embedded system is developed on Raspberry Pi by interfacing a vision camera,an accelerometer,a proximity sensor,and a Global Positioning System(GPS)sensors(i.e.,multi-sensors).The developed embedded system prototype is tested in real-time onsite by installing it on a Railway Inspection Trolley(RIT),which runs at an average speed of 15 km/h.The functional verification of the proposed system is done successfully by detecting and recording the various railway track surface faults.An unhealthy frame’s onsite detection processing time was recorded at approximately 25.6ms.The proposed system can synchronize the acceleration data on specific railway track deformation.The proposed novel embedded system may be beneficial for detecting faults to overcome the conventional manual railway track condition monitoring,which is still being practiced in various developing or underdeveloped countries.展开更多
Background With the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emerge...Background With the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emergency evacuation scenarios. Correctly and effectively evacuating crowds in virtual emergency scenarios are becoming increasingly urgent. One good solution is to extract pedestrian trajectories from videos of emergency situations using a multi-target tracking algorithm and use them to define evacuation procedures. Methods To implement this solution, a trajectory extraction and optimization framework based on multi-target tracking is developed in this study. First, a multi-target tracking algorithm is used to extract and preprocess the trajectory data of the crowd in a video. Then, the trajectory is optimized by combining the trajectory point extraction algorithm and Savitzky-Golay smoothing filtering method. Finally, related experiments are conducted, and the results show that the proposed approach can effectively and accurately extract the trajectories of multiple target objects in real time. Results In addition, the proposed approach retains the real characteristics of the trajectories as much as possible while improving the trajectory smoothing index, which can provide data support for the analysis of pedestrian trajectory data and formulation of personnel evacuation schemes in emergency scenarios. Conclusions Further comparisons with methods used in related studies confirm the feasibility and superiority of the proposed framework.展开更多
This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework i...This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor. First, the real-time data flow （underwater acoustic images） is pre-processed to form the whole underwater acoustic image, and the relevant position information of objects is extracted and determined. An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method. Second, a representation of region information is created in light of the Gaussian particle filter. The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness. Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed. They show that the proposed method can detect and track the moving objects underwater online, and it is effective and robust.展开更多
Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability,...Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the AdaBoost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost.展开更多
Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It a...Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking tasks.These tasks have been considered challenging and high-level perceptual problems,frequently dominated by relative information about the environment,where main concerns such as occlusion,illumination,background,object deformation,and object class variations are commonplace.In order to show the importance of top view surveillance,a collaborative robotics framework has been presented.It can assist in the detection and tracking of multiple objects in top view surveillance.The framework consists of a smart robotic camera embedded with the visual processing unit.The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization.The detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and BOOSTING.These algorithms,along with detection models,help to track and predict the trajectories of detected objects.The pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data set.The detection models achieved maximum True Detection Rate 93%to 90%with a maximum 0.6%False Detection Rate.The tracking results of different algorithms are nearly identical,with tracking accuracy ranging from 90%to 94%.Furthermore,a discussion has been carried out on output results along with future guidelines.展开更多
Single-pass is commonly used in topic detection and tracking( TDT) due to its simplicity,high efficiency and low cost. When dealing with large-scale data,time cost will increase sharply and clustering performance will...Single-pass is commonly used in topic detection and tracking( TDT) due to its simplicity,high efficiency and low cost. When dealing with large-scale data,time cost will increase sharply and clustering performance will be affected greatly. Aiming at this problem,hierarchical clustering algorithm based on single-pass is proposed,which is inspired by hierarchical and concurrent ideas to divide clustering process into three stages. News reports are classified into different categories firstly.Then there are twice single-pass clustering processes in the same category,and one agglomerative clustering among different categories. In addition,for semantic similarity in news reports,topic model is improved based on named entities. Experimental results show that the proposed method can effectively accelerate the process as well as improve the performance.展开更多
Vision-based player recognition is critical in sports applications.Accuracy,efficiency,and Low memory utilization is alluring for ongoing errands,for example,astute communicates and occasion classification.We develope...Vision-based player recognition is critical in sports applications.Accuracy,efficiency,and Low memory utilization is alluring for ongoing errands,for example,astute communicates and occasion classification.We developed an algorithm that tracks the movements of different players from a video of a basketball game.With their position tracked,we then proceed to map the position of these players onto an image of a basketball court.The purpose of tracking player is to provide the maximum amount of information to basketball coaches and organizations,so that they can better design mechanisms of defence and attack.Overall,our model has a high degree of identification and tracking of the players in the court.We directed investigations on soccer,basketball,ice hockey and pedestrian datasets.The trial comes about an exhibit that our technique can precisely recognize players under testing conditions.Contrasted and CNNs that are adjusted from general question identification systems,for example,Faster-RCNN,our approach accomplishes cutting edge exactness on three sorts of recreations(basketball,soccer and ice hockey)with 1000×fewer parameters.The all-inclusive statement of our technique is additionally shown on a standard passer-by recognition dataset in which our strategy accomplishes aggressive execution contrasted and cutting-edge methods.展开更多
Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output(MIMO) radar sys...Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output(MIMO) radar system, especially in the hostile environment. In such conditions, an efficient subarray selection strategy is proposed for MIMO radar performing tasks of target tracking and detection. The goal of the proposed strategy is to minimize the worst-case predicted posterior Cramer-Rao lower bound(PCRLB) while maximizing the detection probability for a certain region. It is shown that the subarray selection problem is NP-hard, and a modified particle swarm optimization(MPSO) algorithm is developed as the solution strategy. A large number of simulations verify that the MPSO can provide close performance to the exhaustive search(ES) algorithm. Furthermore, the MPSO has the advantages of simpler structure and lower computational complexity than the multi-start local search algorithm.展开更多
In this paper,a non-contact auto-focusing method is proposed for the essential function of auto-focusing in mobile devices.Firstly,we introduce an effective target detection method combining the 3-frame difference alg...In this paper,a non-contact auto-focusing method is proposed for the essential function of auto-focusing in mobile devices.Firstly,we introduce an effective target detection method combining the 3-frame difference algorithm and Gauss mixture model,which is robust for complex and changing background.Secondly,a stable tracking method is proposed using the local binary patter feature and camshift tracker.Auto-focusing is achieved by using the coordinate obtained during the detection and tracking procedure.Experiments show that the proposed method can deal with complex and changing background.When there exist multiple moving objects,the proposed method also has good detection and tracking performance.The proposed method implements high efficiency,which means it can be easily used in real mobile device systems.展开更多
Radar and LiDAR are two environmental sensors commonly used in autonomous vehicles,Lidars are accurate in determining objects’positions but significantly less accurate as Radars on measuring their velocities.However,...Radar and LiDAR are two environmental sensors commonly used in autonomous vehicles,Lidars are accurate in determining objects’positions but significantly less accurate as Radars on measuring their velocities.However,Radars relative to Lidars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution.In order to compensate for the low detection accuracy,incomplete target attributes and poor environmental adaptability of single sensors such as Radar and LiDAR,in this paper,an effective method for high-precision detection and tracking of surrounding targets of autonomous vehicles.By employing the Unscented Kalman Filter,Radar and LiDAR information is effectively fused to achieve high-precision detection of the position and speed information of targets around the autonomous vehicle.Finally,the real vehicle test under various driving environment scenarios is carried out.The experimental results show that the proposed sensor fusion method can effectively detect and track the vehicle peripheral targets with high accuracy.Compared with a single sensor,it has obvious advantages and can improve the intelligence level of autonomous cars.展开更多
文摘On grounds of the advent of real-time applications,like autonomous driving,visual surveillance,and sports analysis,there is an augmenting focus of attention towards Multiple-Object Tracking(MOT).The tracking-by-detection paradigm,a commonly utilized approach,connects the existing recognition hypotheses to the formerly assessed object trajectories by comparing the simila-rities of the appearance or the motion between them.For an efficient detection and tracking of the numerous objects in a complex environment,a Pearson Simi-larity-centred Kuhn-Munkres(PS-KM)algorithm was proposed in the present study.In this light,the input videos were,initially,gathered from the MOT dataset and converted into frames.The background subtraction occurred whichfiltered the inappropriate data concerning the frames after the frame conversion stage.Then,the extraction of features from the frames was executed.Afterwards,the higher dimensional features were transformed into lower-dimensional features,and feature reduction process was performed with the aid of Information Gain-centred Singular Value Decomposition(IG-SVD).Next,using the Modified Recurrent Neural Network(MRNN)method,classification was executed which identified the categories of the objects additionally.The PS-KM algorithm identi-fied that the recognized objects were tracked.Finally,the experimental outcomes exhibited that numerous targets were precisely tracked by the proposed system with 97%accuracy with a low false positive rate(FPR)of 2.3%.It was also proved that the present techniques viz.RNN,CNN,and KNN,were effective with regard to the existing models.
基金Supported by National Key Research and Development Program of China(Grant No.2021YFB1600402)National Natural Science Foundation of China(Grant No.52072212)+1 种基金Dongfeng USharing Technology Co.,Ltd.,China Intelli‑gent and Connected Vehicles(Beijing)Research Institute Co.,Ltd.“Shuimu Tsinghua Scholarship”of Tsinghua University of China.
文摘Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.
文摘As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)program(IITP-2023-RS-2022-00156326)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation).
文摘Unmanned aerial vehicles(UAVs)can be used to monitor traffic in a variety of settings,including security,traffic surveillance,and traffic control.Numerous academics have been drawn to this topic because of the challenges and the large variety of applications.This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it.It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile.The goal of this study is to develop a method that first extracts the region of interest(ROI),then finds and tracks the items of interest.The suggested system is divided into six stages.The photos from the obtained dataset are appropriately georeferenced to their actual locations in the first phase,after which they are all co-registered.The ROI,or road and its objects,are retrieved using the GrabCut method in the second phase.The third phase entails data preparation.The segmented images’noise is eliminated using Gaussian blur,after which the images are changed to grayscale and forwarded to the following stage for additional morphological procedures.The YOLOv3 algorithm is used in the fourth step to find any automobiles in the photos.Following that,the Kalman filter and centroid tracking are used to perform the tracking of the detected cars.The Lucas-Kanade method is then used to perform the trajectory analysis on the vehicles.The suggested model is put to the test and assessed using the Vehicle Aerial Imaging from Drone(VAID)dataset.For detection and tracking,the model was able to attain accuracy levels of 96.7%and 91.6%,respectively.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2023-2018-0-01426)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)+2 种基金Also,this work was partially supported by the Taif University Researchers Supporting Project Number(TURSP-2020/115)Taif University,Taif,Saudi Arabia.This work was also supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R239)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Pedestrian detection and tracking are vital elements of today’s surveillance systems,which make daily life safe for humans.Thus,human detection and visualization have become essential inventions in the field of computer vision.Hence,developing a surveillance system with multiple object recognition and tracking,especially in low light and night-time,is still challenging.Therefore,we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at night.In particular,we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared(IR)images using machine learning and tracking them using particle filters.Moreover,a random forest classifier is adopted for image segmentation to identify pedestrians in an image.The result of detection is investigated by particle filter to solve pedestrian tracking.Through the extensive experiment,our system shows 93%segmentation accuracy using a random forest algorithm that demonstrates high accuracy for background and roof classes.Moreover,the system achieved a detection accuracy of 90%usingmultiple templatematching techniques and 81%accuracy for pedestrian tracking.Furthermore,our system can identify that the detected object is a human.Hence,our system provided the best results compared to the state-ofart systems,which proves the effectiveness of the techniques used for image segmentation,classification,and tracking.The presented method is applicable for human detection/tracking,crowd analysis,and monitoring pedestrians in IR video surveillance.
文摘Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so on.Besides,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety.The recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,etc.In this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian walkways.The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,etc.The proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition process.For object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized objects.For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)models.The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.
文摘At present days,object detection and tracking concepts have gained more importance among researchers and business people.Presently,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking process.This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)model.The AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT model.The AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast R-CNN.The RPN is a full convolution network that concurrently predicts the bounding box and score of different objects.The RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection process.Besides,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature maps.The performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight decay.Finally,softmax layer is applied to classify the images.The performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes place.The outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.
基金supported in part by the National Key Research and Development Program of China under Grant 2018YFA0702501in part by NSFC under Grant 41974126,41674116 and 42004101。
文摘Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.
基金Key Basic Research Project of Strengthening the Foundations Plan of China (Grant No.2019-JCJQ-ZD-360-12)National Defense Basic Scientific Research Program of China (Grant No.JCKY2021208B011)to provide fund for conducting experiments。
文摘High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy,high resolution and high efficiency.However,it faces challenge in dense objects tracking and 3D trajectories reconstruction due to the characteristics of small size and dense distribution of fragment swarm.To address these challenges,this work presents a warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography.Firstly,background difference algorithm is utilized to extract the center and area of each fragment in the image sequence.Subsequently,a multi-object tracking(MOT)algorithm using Kalman filtering and Hungarian optimal assignment is developed to realize real-time and robust trajectories tracking of fragment swarm.To reconstruct 3D motion trajectories,a global stereo trajectories matching strategy is presented,which takes advantages of epipolar constraint and continuity constraint to correctly retrieve stereo correspondence followed by 3D trajectories refinement using polynomial fitting.Finally,the simulation and experimental results demonstrate that the proposed method can accurately track the motion trajectories and reconstruct the spatio-temporal distribution of 1.0×10^(3)fragments in a field of view(FOV)of 3.2 m×2.5 m,and the accuracy of the velocity estimation can achieve 98.6%.
基金supported by the National Natural Science Foundation of China (No.61971412)。
文摘Underwater monopulse space-time adaptive track-before-detect method,which combines space-time adaptive detector(STAD)and the track-before-detect algorithm based on dynamic programming(DP-TBD),denoted as STAD-DP-TBD,can effectively detect low-speed weak targets.However,due to the complexity and variability of the underwater environment,it is difficult to obtain sufficient secondary data,resulting in a serious decline in the detection and tracking performance,and leading to poor robustness of the algorithm.In this paper,based on the adaptive matched filter(AMF)test and the RAO test,underwater monopulse AMF-DP-TBD algorithm and RAO-DP-TBD algorithm which incorporate persymmetry and symmetric spectrum,denoted as PSAMF-DP-TBD and PS-RAO-DP-TBD,are proposed and compared with the AMF-DP-TBD algorithm and RAO-DP-TBD algorithm based on persymmetry array,denoted as P-AMF-DP-TBD and P-RAO-DP-TBD.The simulation results show that the four methods can work normally with sufficient secondary data and slightly insufficient secondary data,but when the secondary data is severely insufficient,the P-AMF-DP-TBD and P-RAO-DP-TBD algorithms has failed while the PSAMF-DP-TBD and PS-RAO-DP-TBD algorithms still have good detection and tracking capabilities.
基金supported by the NCRA project of the Higher Education Commission Pakistan.
文摘Derailment of trains is not unusual all around the world,especially in developing countries,due to unidentified track or rolling stock faults that cause massive casualties each year.For this purpose,a proper condition monitoring system is essential to avoid accidents and heavy losses.Generally,the detection and classification of railway track surface faults in real-time requires massive computational processing and memory resources and is prone to a noisy environment.Therefore,in this paper,we present the development of a novel embedded system prototype for condition monitoring of railway track.The proposed prototype system works in real-time by acquiring railway track surface images and performing two tasks a)detect deformation(i.e.,faults)like squats,shelling,and spalling using the contour feature algorithm and b)the vibration signature on that faulty spot by synchronizing acceleration and image data.A new illumination scheme is also proposed to avoid the sunlight reflection that badly affects the image acquisition process.The contour detection algorithm is applied here to detect the uneven shapes and discontinuities in the geometrical structure of the railway track surface,which ultimately detects unhealthy regions.It works by converting Red,Green,and Blue(RGB)images into binary images,which distinguishes the unhealthy regions by making them white color while the healthy regions in black color.We have used the multiprocessing technique to overcome the massive processing and memory issues.This embedded system is developed on Raspberry Pi by interfacing a vision camera,an accelerometer,a proximity sensor,and a Global Positioning System(GPS)sensors(i.e.,multi-sensors).The developed embedded system prototype is tested in real-time onsite by installing it on a Railway Inspection Trolley(RIT),which runs at an average speed of 15 km/h.The functional verification of the proposed system is done successfully by detecting and recording the various railway track surface faults.An unhealthy frame’s onsite detection processing time was recorded at approximately 25.6ms.The proposed system can synchronize the acceleration data on specific railway track deformation.The proposed novel embedded system may be beneficial for detecting faults to overcome the conventional manual railway track condition monitoring,which is still being practiced in various developing or underdeveloped countries.
文摘Background With the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emergency evacuation scenarios. Correctly and effectively evacuating crowds in virtual emergency scenarios are becoming increasingly urgent. One good solution is to extract pedestrian trajectories from videos of emergency situations using a multi-target tracking algorithm and use them to define evacuation procedures. Methods To implement this solution, a trajectory extraction and optimization framework based on multi-target tracking is developed in this study. First, a multi-target tracking algorithm is used to extract and preprocess the trajectory data of the crowd in a video. Then, the trajectory is optimized by combining the trajectory point extraction algorithm and Savitzky-Golay smoothing filtering method. Finally, related experiments are conducted, and the results show that the proposed approach can effectively and accurately extract the trajectories of multiple target objects in real time. Results In addition, the proposed approach retains the real characteristics of the trajectories as much as possible while improving the trajectory smoothing index, which can provide data support for the analysis of pedestrian trajectory data and formulation of personnel evacuation schemes in emergency scenarios. Conclusions Further comparisons with methods used in related studies confirm the feasibility and superiority of the proposed framework.
基金supported by the National Natural Science Foundation of China(Grant No.51009040)Heilongjiang Postdoctoral Fund(Grant No.LBH-Z11205)+1 种基金the National High Technology Research and Development Program of China(863 Program,Grant No.2011AA09A106)the China Postdoctoral Science Foundation(Grant No.2012M510928)
文摘This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor. First, the real-time data flow （underwater acoustic images） is pre-processed to form the whole underwater acoustic image, and the relevant position information of objects is extracted and determined. An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method. Second, a representation of region information is created in light of the Gaussian particle filter. The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness. Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed. They show that the proposed method can detect and track the moving objects underwater online, and it is effective and robust.
基金Supported by Open Research Fund of State Key Laboratory of Advanced Technology for Vehicle Body Design & Manufacture of China (Grant No.61075002)Hunan Provincial Natural Science Foundation of China (Grant No.13JJ4033)
文摘Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the AdaBoost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost.
基金the Framework of International Cooperation Program managed by the National Research Foundation of Korea(2019K1A3A1A8011295711).
文摘Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking tasks.These tasks have been considered challenging and high-level perceptual problems,frequently dominated by relative information about the environment,where main concerns such as occlusion,illumination,background,object deformation,and object class variations are commonplace.In order to show the importance of top view surveillance,a collaborative robotics framework has been presented.It can assist in the detection and tracking of multiple objects in top view surveillance.The framework consists of a smart robotic camera embedded with the visual processing unit.The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization.The detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and BOOSTING.These algorithms,along with detection models,help to track and predict the trajectories of detected objects.The pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data set.The detection models achieved maximum True Detection Rate 93%to 90%with a maximum 0.6%False Detection Rate.The tracking results of different algorithms are nearly identical,with tracking accuracy ranging from 90%to 94%.Furthermore,a discussion has been carried out on output results along with future guidelines.
基金Supported by the National Natural Science Foundation of China(No.61502312)the Fundamental Research Funds for the Central Universities(No.2017BQ024)+1 种基金the Natural Science Foundation of Guangdong Province(No.2017A030310428)the Science and Technology Programm of Guangzhou(No.201806020075,20180210025)
文摘Single-pass is commonly used in topic detection and tracking( TDT) due to its simplicity,high efficiency and low cost. When dealing with large-scale data,time cost will increase sharply and clustering performance will be affected greatly. Aiming at this problem,hierarchical clustering algorithm based on single-pass is proposed,which is inspired by hierarchical and concurrent ideas to divide clustering process into three stages. News reports are classified into different categories firstly.Then there are twice single-pass clustering processes in the same category,and one agglomerative clustering among different categories. In addition,for semantic similarity in news reports,topic model is improved based on named entities. Experimental results show that the proposed method can effectively accelerate the process as well as improve the performance.
文摘Vision-based player recognition is critical in sports applications.Accuracy,efficiency,and Low memory utilization is alluring for ongoing errands,for example,astute communicates and occasion classification.We developed an algorithm that tracks the movements of different players from a video of a basketball game.With their position tracked,we then proceed to map the position of these players onto an image of a basketball court.The purpose of tracking player is to provide the maximum amount of information to basketball coaches and organizations,so that they can better design mechanisms of defence and attack.Overall,our model has a high degree of identification and tracking of the players in the court.We directed investigations on soccer,basketball,ice hockey and pedestrian datasets.The trial comes about an exhibit that our technique can precisely recognize players under testing conditions.Contrasted and CNNs that are adjusted from general question identification systems,for example,Faster-RCNN,our approach accomplishes cutting edge exactness on three sorts of recreations(basketball,soccer and ice hockey)with 1000×fewer parameters.The all-inclusive statement of our technique is additionally shown on a standard passer-by recognition dataset in which our strategy accomplishes aggressive execution contrasted and cutting-edge methods.
基金supported by the National Natural Science Foundation of China(61601504)。
文摘Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output(MIMO) radar system, especially in the hostile environment. In such conditions, an efficient subarray selection strategy is proposed for MIMO radar performing tasks of target tracking and detection. The goal of the proposed strategy is to minimize the worst-case predicted posterior Cramer-Rao lower bound(PCRLB) while maximizing the detection probability for a certain region. It is shown that the subarray selection problem is NP-hard, and a modified particle swarm optimization(MPSO) algorithm is developed as the solution strategy. A large number of simulations verify that the MPSO can provide close performance to the exhaustive search(ES) algorithm. Furthermore, the MPSO has the advantages of simpler structure and lower computational complexity than the multi-start local search algorithm.
基金supported by ZTE Industry-Academia-Research Cooperation Funds
文摘In this paper,a non-contact auto-focusing method is proposed for the essential function of auto-focusing in mobile devices.Firstly,we introduce an effective target detection method combining the 3-frame difference algorithm and Gauss mixture model,which is robust for complex and changing background.Secondly,a stable tracking method is proposed using the local binary patter feature and camshift tracker.Auto-focusing is achieved by using the coordinate obtained during the detection and tracking procedure.Experiments show that the proposed method can deal with complex and changing background.When there exist multiple moving objects,the proposed method also has good detection and tracking performance.The proposed method implements high efficiency,which means it can be easily used in real mobile device systems.
基金Supported by National Natural Science Foundation of China(Grant Nos.U20A20333,61906076,51875255,U1764257,U1762264),Jiangsu Provincial Natural Science Foundation of China(Grant Nos.BK20180100,BK20190853)Six Talent Peaks Project of Jiangsu Province(Grant No.2018-TD-GDZB-022)+1 种基金China Postdoctoral Science Foundation(Grant No.2020T130258)Jiangsu Provincial Key Research and Development Program of China(Grant No.BE2020083-2).
文摘Radar and LiDAR are two environmental sensors commonly used in autonomous vehicles,Lidars are accurate in determining objects’positions but significantly less accurate as Radars on measuring their velocities.However,Radars relative to Lidars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution.In order to compensate for the low detection accuracy,incomplete target attributes and poor environmental adaptability of single sensors such as Radar and LiDAR,in this paper,an effective method for high-precision detection and tracking of surrounding targets of autonomous vehicles.By employing the Unscented Kalman Filter,Radar and LiDAR information is effectively fused to achieve high-precision detection of the position and speed information of targets around the autonomous vehicle.Finally,the real vehicle test under various driving environment scenarios is carried out.The experimental results show that the proposed sensor fusion method can effectively detect and track the vehicle peripheral targets with high accuracy.Compared with a single sensor,it has obvious advantages and can improve the intelligence level of autonomous cars.
