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Robust People Detection and Tracking in a Multi-Camera Indoor Visual Surveillance System

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4 Author(s)
Tao Yang ; FX Palo Alto Laboratory, Palo Alto, CA, 94304, USA; Northwestern Polytechnical University, Xi'an, 710072, China. yangtaonwpu@163.com ; Francine Chen ; Don Kimber ; Jim Vaughan

In this paper we describe the analysis component of an indoor, real-time, multi-camera surveillance system. The analysis includes: (1) a novel feature-level foreground segmentation method which achieves efficient and reliable segmentation results even under complex conditions, (2) an efficient greedy search based approach for tracking multiple people through occlusion, and (3) a method for multi-camera handoff that associates individual trajectories in adjacent cameras. The analysis is used for an 18 camera surveillance system that has been running continuously in an indoor business over the past several months. Our experiments demonstrate that the processing method for people detection and tracking across multiple cameras is fast and robust.

Published in:

2007 IEEE International Conference on Multimedia and Expo

Date of Conference:

2-5 July 2007