Slow feature analysis for multi-camera activity understanding

Lei Zhang, Xiaoqiang Lu, Yuan Yuan

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Multi-camera activity analysis is a key point in video surveillance of many wide-area scenes, such as airports, underground stations, shopping mall and road junctions. On the basis of previous work, this paper presents a new feature learning method based on Slow Feature Analysis (SFA) to understand activities observed across the network of cameras. The main contribution of this paper can be summarized as follows: (1) It is the first time that SFA-based learning method is introduced to multi-camera activity understanding; (2) It presents an evaluation to examine the effectiveness of SFA-based method to facilitate the learning of inter-camera activity pattern dependencies; and (3) It estimates the sensitivity of learning inter-camera time delayed dependency given different training size, which is a critical factor for accurate dependency learning and has not been largely studied by existing work before. Experiments are carried out on a dataset obtained in a trident roadway. The results demonstrate that the SFA-based method outperforms the sate of the art.

源语言英语
主期刊名Proceedings - 2013 International Conference on Virtual Reality and Visualization, ICVRV 2013
出版商IEEE Computer Society
241-244
页数4
ISBN(印刷版)9780769551500
DOI
出版状态已出版 - 2013
已对外发布
活动2013 International Conference on Virtual Reality and Visualization, ICVRV 2013 - Xi'an, Shaanxi, 中国
期限: 14 9月 201315 9月 2013

出版系列

姓名Proceedings - 2013 International Conference on Virtual Reality and Visualization, ICVRV 2013

会议

会议2013 International Conference on Virtual Reality and Visualization, ICVRV 2013
国家/地区中国
Xi'an, Shaanxi
时期14/09/1315/09/13

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