Occlusion reasoning for tracking multiple people

  • Authors:

  • Weiming Hu;Xue Zhou;Min Hu;Steve Maybank

  • Affiliations:

  • National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing;School of Computer Science and Information Systems, Birkbeck College, London, UK

  • Venue:
  • IEEE Transactions on Circuits and Systems for Video Technology
  • Year:
  • 2009

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Abstract

Occlusion reasoning is one of the most challenging issues in visual surveillance. In this letter, we propose a new approach for reasoning about occlusions between multiple people. In our approach, occlusion relationships between people are explicitly defined and deduction of the occlusion relationships is integrated into the whole tracking framework. The prior knowledge is supplied by a set of models which include a 2-D elliptical shape model, a spatial-color mixture of Gaussians appearance model, and a motion model with constant velocity. An observation likelihood function is constructed based on the similarity between the observations and the object appearance models with given states. The occlusion relationships are deduced from the current states of the objects and the current observations, using the observation likelihood function. The previous occlusion relationships are not required for deducing the current occlusion relationships. The problem of tracking and occlusion reasoning for more than two people is formulated mathematically, and a solution is proposed based on particle filtering. Experimental results on several real video sequences from indoor and outdoor scenes show the effectiveness of our approach.