Laser-based intelligent surveillance and abnormality detection in extremely crowded scenarios

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in the extremely crowded area has become an urgent need for public security. In this paper, we propose a novel laser-based system which can simultaneously perform the tr...

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Bibliographic Details
Published in2012 IEEE International Conference on Robotics and Automation pp. 2170 - 2176
Main Authors Xuan Song, Xiaowei Shao, Quanshi Zhang, Shibasaki, R., Huijing Zhao, Hongbin Zha
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2012
Subjects
Computational modeling
Force
Legged locomotion
Robustness
Semantics
Surveillance
Trajectory
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Summary:Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in the extremely crowded area has become an urgent need for public security. In this paper, we propose a novel laser-based system which can simultaneously perform the tracking, semantic scene learning and abnormality detection in the large and crowded environment. In our system, a novel abnormality detection model is proposed, and it considers and combines various factors that will influence human activity. Moreover, this model intensively investigate the relationship between pedestrians' social behaviors and their walking scenarios. We successfully applied the proposed system to the JR subway station of Tokyo, which can cover a 60×35m area, robustly track more than 180 targets at the same time and simultaneously perform the online semantic scene learning and abnormality detection with no human intervention.
ISBN:9781467314039
146731403X
ISSN:1050-4729
2577-087X
DOI:10.1109/ICRA.2012.6224827