ICRA 2011 Paper Abstract


Paper TuP104.5

Song, Xuan (University of Tokyo), Shao, Xiaowei (University of Tokyo), Shibasaki, Ryosuke (University of Tokyo), Zhao, Huijing (Peking University), Cui, Jinshi (Peking University), Zha, Hongbin (Peking University)

A Novel Laser-Based System: Fully Online Detection of Abnormal Activity Via an Unsupervised Method

Scheduled for presentation during the Regular Sessions "Human Detection and Tracking II" (TuP104), Tuesday, May 10, 2011, 14:40−14:55, Room 3E

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Surveillance Systems, Intrusion Detection, Identification and Security, Human detection & tracking


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 6035m area, track more 150 targets at the same time and simultaneously perform the robust detection of abnormal activity with no human intervention.



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