tailieunhanh - Báo cáo hóa học: " Research Article Distributed Bayesian Multiple-Target Tracking in Crowded Environments Using Multiple Collaborative Cameras"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Distributed Bayesian Multiple-Target Tracking in Crowded Environments Using Multiple Collaborative Cameras | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 38373 15 pages doi 2007 38373 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. 1. 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 .