Multiple People Visual Tracking in a Multi-Camera System for Cluttered Environments
Yu-Shan Cheng, Cheng‐Ming Huang, Li‐Chen Fu · 2006
This paper aims to track multiple people in a multicamera system for cluttered environment which can be divided into two important parts: one is tracking multiple people in a single camera environment, and the other is tracking multiple people in a multi-camera environment. In a single camera environment, we apply the motion detector and the ellipse algorithm to detect a new person intruding the surveillance area. Then, we utilize the template matching and the ellipse matching to track the person. To prevent tracking failure when people cross over each other, we include the hereby proposed joint visual probabilistic data association filter (JVPDAF) to track multiple people successfully. In a multi-camera environment, the major problem is to determine whether the new person intruding into some surveillance area of a camera is an identified person by some other camera or not. To resolve the aforementioned problem, we propose an approach called consistence labeling. After such labeling process, we track this person by the JVPDA algorithm. Finally, effectiveness of this tracking algorithm is validated via extensive experiments