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Sensor planning for automated and persistent object tracking with multiple cameras

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6 Author(s)
Yi Yao ; Imaging, Robotics, and Intelligent Systems (IRIS) Lab, University of Tennessee, Knoxville, USA ; Chung-Hao Chen ; Besma Abidi ; David Page
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Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility, a fundamental requirement of object tracking, is insufficient for persistent and automated tracking. In such applications, a continuous and consistently labeled trajectory of the same object should be maintained across different cameraspsila views. Therefore, a sufficient overlap between the cameraspsila FOVs should be secured so that camera handoff can be executed successfully and automatically before the object of interest becomes untraceable or unidentifiable. The proposed sensor planning method improves existing algorithms by adding handoff rate analysis, which preserves necessary overlapped FOVs for an optimal handoff success rate. In addition, special considerations such as resolution and frontal view requirements are addressed using two approaches: direct constraint and adaptive weight. The resulting camera placement is compared with a reference algorithm by Erdem and Sclaroff. Significantly improved handoff success rate and frontal view percentage are illustrated via experiments using typical office floor plans.

Published in:

Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on

Date of Conference:

23-28 June 2008