1 Introduction Tieshan Syenite crosses between Dongfeng and Zhangyuan’an in Zhenghe of Fujian province,occurs in the direction of 42°,Total length 8500m,width 600-800m and its Area of about 39km2.Outcrops of the...1 Introduction Tieshan Syenite crosses between Dongfeng and Zhangyuan’an in Zhenghe of Fujian province,occurs in the direction of 42°,Total length 8500m,width 600-800m and its Area of about 39km2.Outcrops of the mass are展开更多
In pedestrian re-recognition,the traditional pedestrian re-recognition method will be affected by the changes of background,veil,clothing and so on,which will make the recognition effect decline.In order to reduce the...In pedestrian re-recognition,the traditional pedestrian re-recognition method will be affected by the changes of background,veil,clothing and so on,which will make the recognition effect decline.In order to reduce the impact of background,veil,clothing and other changes on the recognition effect,this paper proposes a pedestrian re-recognition method based on the cycle-consistent generative adversarial network and multifeature fusion.By comparing the measured distance between two pedestrians,pedestrian re-recognition is accomplished.Firstly,this paper uses Cycle GAN to transform and expand the data set,so as to reduce the influence of pedestrian posture changes as much as possible.The method consists of two branches:global feature extraction and local feature extraction.Then the global feature and local feature are fused.The fused features are used for comparison measurement learning,and the similarity scores are calculated to sort the samples.A large number of experimental results on large data sets CUHK03 and VIPER show that this new method reduces the influence of background,veil,clothing and other changes on the recognition effect.展开更多
With the rapid development of related computer vision algorithms,the large-scale use of video surveillance systems has not only improved traffic safety,but also promoted the development of intelligent high-speed.Howev...With the rapid development of related computer vision algorithms,the large-scale use of video surveillance systems has not only improved traffic safety,but also promoted the development of intelligent high-speed.However,due to the complexity of the application scene,especially in the face of complex scene occlusion factors,the noise generated by the occlusion inevitably leads to the loss of the feature information of the identified person or object,which poses a great challenge to the existing pedestrian re-recognition algorithms.Therefore,this paper proposes a novel pedestrian re-recognition based on hybrid network.Feature extraction is carried out on four cooperative branches:local branch,global branch,global contrast pool branch and associated branch,and powerful diversity pedestrian feature expression ability is obtained.The network in this paper can be applied to different backbone networks.Through experimental comparison,the proposed algorithm has certain advantages compared with the latest methods,and the ablation experimental analysis further proves the effectiveness of the proposed network structure.展开更多
基金supported by Zhejiang-Fujian Songzheng ore survey project (Project Number 1212011120882)Research Demonstration Project of Copper Polymetallic Ore (Project Number 12120114002601)
文摘1 Introduction Tieshan Syenite crosses between Dongfeng and Zhangyuan’an in Zhenghe of Fujian province,occurs in the direction of 42°,Total length 8500m,width 600-800m and its Area of about 39km2.Outcrops of the mass are
文摘In pedestrian re-recognition,the traditional pedestrian re-recognition method will be affected by the changes of background,veil,clothing and so on,which will make the recognition effect decline.In order to reduce the impact of background,veil,clothing and other changes on the recognition effect,this paper proposes a pedestrian re-recognition method based on the cycle-consistent generative adversarial network and multifeature fusion.By comparing the measured distance between two pedestrians,pedestrian re-recognition is accomplished.Firstly,this paper uses Cycle GAN to transform and expand the data set,so as to reduce the influence of pedestrian posture changes as much as possible.The method consists of two branches:global feature extraction and local feature extraction.Then the global feature and local feature are fused.The fused features are used for comparison measurement learning,and the similarity scores are calculated to sort the samples.A large number of experimental results on large data sets CUHK03 and VIPER show that this new method reduces the influence of background,veil,clothing and other changes on the recognition effect.
文摘With the rapid development of related computer vision algorithms,the large-scale use of video surveillance systems has not only improved traffic safety,but also promoted the development of intelligent high-speed.However,due to the complexity of the application scene,especially in the face of complex scene occlusion factors,the noise generated by the occlusion inevitably leads to the loss of the feature information of the identified person or object,which poses a great challenge to the existing pedestrian re-recognition algorithms.Therefore,this paper proposes a novel pedestrian re-recognition based on hybrid network.Feature extraction is carried out on four cooperative branches:local branch,global branch,global contrast pool branch and associated branch,and powerful diversity pedestrian feature expression ability is obtained.The network in this paper can be applied to different backbone networks.Through experimental comparison,the proposed algorithm has certain advantages compared with the latest methods,and the ablation experimental analysis further proves the effectiveness of the proposed network structure.
