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Paper WeA09.3

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

Laser-Based Intelligent Surveillance and Abnormality Detection in Extremely Crowded Scenarios

Scheduled for presentation during the Regular Session "Surveillance" (WeA09), Wednesday, May 16, 2012, 09:00−09:15, Meeting Room 9 (Sa)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on October 24, 2017

Keywords Surveillance Systems, Human Detection & Tracking, Intrusion Detection, Identification and Security

Abstract

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.

 

 

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