Distributed Bayesian Multiple-Target Tracking in Crowded Environments Using Multiple Collaborative Cameras
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Research Article Distributed Bayesian Multiple-Target Tracking in Crowded Environments Using Multiple Collaborative Cameras Wei Qu,1 Dan Schonfeld,1 and Magdi Mohamed2 1 Multimedia
Communications Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Chicago, IL 60607-7053, USA 2 Visual Communication and Display Technologies Lab, Physical Realization Research COE, Motorola Labs, Schaumburg, IL 60196, USA Received 28 September 2005; Revised 13 March 2006; Accepted 15 March 2006 Recommended by Justus Piater Multiple-target tracking has received tremendous attention due to its wide practical applicability in video processing and analysis applications. Most existing techniques, however, suffer from the well-known “multitarget occlusion” problem and/or immense computational cost due to its use of high-dimensional joint-state representations. In this paper, we present a distributed Bayesian framework using multiple collaborative cameras for robust and efficient multiple-target tracking in crowded environments with significant and persistent occlusion. When the targets are in close proximity or present multitarget occlusions in a particular camera view, camera collaboration between different views is activated in order to handle the multitarget occlusion problem in an innovative way. Specifically, we propose to model the camera collaboration likelihood density by using epipolar geometry with sequential Monte Carlo implementation. Experimental results have been demonstrated for both synthetic and real-world video data. Copyright © 2007 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION AND RELATED WORK
Visual multiple-target tracking (MTT) has received tremendous attention in the video processing community due to its numerous potential applications in important tasks such as video surveillance, human activity analysis, traffic monitoring, and so forth. MTT for targets whose appearance is distinctive is much easier since it can be solved reasonably well by using multiple independent single-target trackers. In this situation, when tracking a specific target, all the other targets can be viewed as background due to their distinct appearance. However, MTT for targets whose appearance is similar or identical such as pedestrians in crowded scenes is a much more difficult task. In addition to all of the challenging problems inherent in single-target tracking, MTT must deal with multitarget occlusion, namely, the tracker must separate the targets and assign them correct labels. Most early efforts for MTT use monocular video. A widely accepted approach that addresses many problems in this difficult task is based on a joint state-space representation and infers the joint data association [1, 2]. MacCormick and Blake [3] used a binary variable to identify foreground objects and proposed a probabilistic exclusion principle to
penalize the hypothesis where two objects occlude. In [4], the likelihood is calculated by enumerating all possible association hypotheses. Isard and MacCormick [5] c
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