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Collaborative Kalman filters for vehicle tracking
CaoXianbin; ShiZhengrong; YanPingkun; LiXuelong; Cao Xianbin
作者部门光学影像分析与学习中心
2011
发表期刊IEEE International Workshop on Machine Learning for Signal Processing
ISSN9781457716232
产权排序3
摘要Airborne vehicle tracking system is receiving increasing attention because of its high mobility and large surveillance scope. However, tracking multiple vehicles simultaneously on airborne platform is a challenging problem, owing to uncertain vehicle motion and visible frame-to-frame jitter caused by camera vibration. To address these problems, a new collaborative tracking framework is proposed. The framework consists of two level tracking processes: to track vehicles as groups, the higher level builds the relevance network and divides target vehicles into different groups; the relevance is calculated based on the status information of vehicles obtained by the lower level. This kind of group tracking takes into account the relevance of vehicles and reduces the impact of camera vibration, so the proposed method is applicable for multi-vehicle tracking in airborne videos. Experimental results demonstrate that the proposed method has better performance in terms of the tracking speed and accuracy compared to other existing approaches.
关键词Group Tracking Relevance Network Kalman Filter Airborne Platforms Multi-target Tracking
收录类别EI
语种英语
项目资助者National Basic Research Program of China (973 Program); National Natural Science Foundation of China; Foundation for Innovative Research Groups of the National Natural Science Foundation of China
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/19862
专题光谱成像技术研究室
通讯作者Cao Xianbin
推荐引用方式
GB/T 7714
CaoXianbin,ShiZhengrong,YanPingkun,et al. Collaborative Kalman filters for vehicle tracking[J]. IEEE International Workshop on Machine Learning for Signal Processing,2011.
APA CaoXianbin,ShiZhengrong,YanPingkun,LiXuelong,&Cao Xianbin.(2011).Collaborative Kalman filters for vehicle tracking.IEEE International Workshop on Machine Learning for Signal Processing.
MLA CaoXianbin,et al."Collaborative Kalman filters for vehicle tracking".IEEE International Workshop on Machine Learning for Signal Processing (2011).
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