Distributed Bayesian multiple-target tracking in crowded environments using multiple collaborative cameras

  • Authors:

  • Wei Qu;Dan Schonfeld;Magdi Mohamed

  • Affiliations:

  • Multimedia Communications Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Chicago, IL;Multimedia Communications Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Chicago, IL;Visual Communication and Display Technologies Lab, Physical Realization Research COE, Motorola Labs, Schaumburg, IL

  • Venue:
  • EURASIP Journal on Applied Signal Processing
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.