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Camera handoff and placement for automated tracking systems with multiple omnidirectional cameras
Affiliation:1. Department of Applied Chemistry, School of Chemical and Engineering, Tianjin University, Tianjin 300072, PR China;2. Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, PR China;3. McNair Technology Company Limited, Dongguan City, Guangdong 523700, PR China;1. Department of Food Science, University of Guelph, 50 Stone Rd. East, Guelph, Ontario N1G 2W1, Canada;2. Department of of Food Physics and Meat Science, Institute of Food Science and Biotechnology, University of Hohenheim, Garbenstrasse 21/25, 70599 Stuttgart, Germany;3. Biopolymers and Colloids Research Laboratory, Department of Food Science, University of Massachucetts Amherst, 240 Chenoweth Laboratory, 102 Holdsworth Way, Amherst, MA 01003, USA
Abstract:In a multi-camera surveillance system, both camera handoff and placement play an important role in generating an automated and persistent object tracking, typical of most surveillance requirements. Camera handoff should comprise three fundamental components, time to trigger handoff process, the execution of consistent labeling, and the selection of the next optimal camera. In this paper, we design an observation measure to quantitatively formulate the effectiveness of object tracking so that we can trigger camera handoff timely and select the next camera appropriately before the tracked object falls out of the field of view (FOV) of the currently observing camera. In the meantime, we present a novel solution to the consistent labeling problem in omnidirectional cameras. A spatial mapping procedure is proposed to consider both the noise inherent to the tracking algorithms used by the system and the lens distortion introduced by omnidirectional cameras. This does not only avoid the tedious process, but also increases the accuracy, to obtain the correspondence between omnidirectional cameras without human interventions. We also propose to use the Wilcoxon Signed-Rank Test to improve the accuracy of trajectory association between pairs of objects. In addition, since we need a certain amount of time to successfully carry out the camera handoff procedure, we introduce an additional constraint to optimally reserve sufficient cameras’ overlapped FOVs for the camera placement. Experiments show that our proposed observation measure can quantitatively formulate the effectiveness of tracking, so that camera handoff can smoothly transfer objects of interest. Meanwhile, our proposed consistent labeling approach can perform as accurately as the geometry-based approach without tedious calibration processes and outperform Calderara’s homography-based approach. Our proposed camera placement method exhibits a significant increase in the camera handoff success rate at the cost of slightly decreased coverage, as compared to Erdem and Sclaroff’s method without considering the requirement on overlapped FOVs.
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