TY - GEN
T1 - Slow feature analysis for multi-camera activity understanding
AU - Zhang, Lei
AU - Lu, Xiaoqiang
AU - Yuan, Yuan
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Multicamera activity analysis
KW - Slow feature analysis
KW - Video surveillance
UR - http://www.scopus.com/inward/record.url?scp=84893254302&partnerID=8YFLogxK
U2 - 10.1109/ICVRV.2013.46
DO - 10.1109/ICVRV.2013.46
M3 - 会议稿件
AN - SCOPUS:84893254302
SN - 9780769551500
T3 - Proceedings - 2013 International Conference on Virtual Reality and Visualization, ICVRV 2013
SP - 241
EP - 244
BT - Proceedings - 2013 International Conference on Virtual Reality and Visualization, ICVRV 2013
PB - IEEE Computer Society
T2 - 2013 International Conference on Virtual Reality and Visualization, ICVRV 2013
Y2 - 14 September 2013 through 15 September 2013
ER -