摘要
针对非刚性运动和大位移场景下运动遮挡检测的准确性与鲁棒性问题,提出一种基于光流与多尺度上下文的图像序列运动遮挡检测方法.首先,设计基于扩张卷积的多尺度上下文信息聚合网络,通过图像序列多尺度上下文信息获取更大范围的图像特征;然后,采用特征金字塔构建基于多尺度上下文与光流的端到端运动遮挡检测网络模型,利用光流优化非刚性运动和大位移区域的运动检测遮挡信息;最后,构造基于运动边缘的网络模型训练损失函数,获取准确的运动遮挡边界.分别采用MPI-Sintel和KITTI测试数据集对所提方法与现有的代表性方法进行实验对比与分析.实验结果表明,所提方法能够有效提高运动遮挡检测的准确性和鲁棒性,尤其在非刚性运动和大位移等困难场景下具有更好的遮挡检测鲁棒性.
In order to improve the accuracy and robustness of occlusion detection under non-rigid motion and large displacements,we propose an occlusion detection method of image sequence motion based on optical flow and multiscale context.First,we design a multiscale context information aggregation network based on dilated convolution which obtains a wider range of image features through multiscale context information of image sequence.Then,we construct an end-to-end motion occlusion detection network model based on multiscale context and optical flow using feature pyramid,utilize the optical flow to optimize the performance of occlusion detection in areas of non-rigid motion and large displacements region.Finally,we present a novel motion edge training loss function to obtain the accurate motion occlusion boundary.We compare and analysis our method with the existing representative approaches by using the MPI-Sintel datasets and KITTI datasets,respectively.The experimental results show that the proposed method can effectively improve the accuracy and robustness of motion occlusion detection,especially gains the better occlusion detection robustness under non-rigid motion and large displacements.
作者
冯诚
张聪炫
陈震
李兵
黎明
FENG Cheng;ZHANG Cong-Xuan;CHEN Zhen;LI Bing;LI Ming(School of Measuring and Optical Engineering,Nanchang Hangkong University,Nanchang 330063;National Key Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190;School of Information Engineering,Nanchang Hangkong University,Nanchang 330063)
出处
《自动化学报》
EI
CAS
CSCD
北大核心
2024年第9期1854-1865,共12页
Acta Automatica Sinica
基金
国家重点研发计划(2020YFC2003800)
国家自然科学基金(61866026,61772255,62222206)
江西省杰出青年人才计划(20192BCB23011)
江西省自然科学基金重点项目(20202ACB214007)
江西省优势科技创新团队(20165BCB19007)资助。
关键词
图像序列
遮挡检测
深度学习
多尺度上下文
非刚性运动
Image sequence
occlusion detection
deep learning
multiscale context
non-rigid motion
