A Novel Laser-based System: Fully Online Detection of Abnormal Activity via an Unsupervised Method
Abnormal activity detection plays a crucial role in surveillance applications, and such system has become an urgent need for public security. In this paper, we propose a novel laser-based system, which can perform the online detection of abnormal activity with an unsupervised way. The proposed system has the following key features that make it advantageous over previous ones: (1) It can cover quite a large and crowded area, such as subway station, public square, intersection and etc. (2) The overall system can vary with time period, incrementally learn the behavior pattern of pedestrians and perform the fully online detection of abnormal activity. This feature makes our system be quite suitable for the real-time applications. (3) The abnormal activity detection is carried out with a fully unsupervised way, there is no need for manual labelling and constructing the huge training datasets. We successfully applied the proposed system into the JR subway station of Tokyo, which can cover a 60×35m area, track more 150 targets at the same time and simultaneously perform the robust detection of abnormal activity with no human intervention.
Xuan Song Xiaowei Shao Ryosuke Shibasaki Huijing Zhao Jinshi Cui Hongbin Zha
Center for Spatial Information Science,The University of Tokyo,Japan Key Laboratory of Machine Perception (MoE),Peking University,China
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
1317-1322
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）