Occlusion handling framework for tracking in smart camera networks by per-target assistance task assignment

Occlusion is one of the most difficult challenges in the area of visual tracking. In this paper, we propose an occlusion handling framework to improve the performance of local tracking in a smart camera view in a multi-camera network. We formulate an extensible energy function to quantify the quality of a camera's observation of a particular target by taking into account of both person--person and object--person occlusion. Using this energy function, a smart camera assesses the quality of observations over all targets being tracked. When it cannot adequately observe of a target, a smart camera estimates the quality of observation of the target from view points of other assisting cameras. If a camera with better observation of the target is found, the tracking task of the target is carried out with the assistance of that camera. In our framework, only positions of persons being tracked are exchanged between smart cameras. Thus communication bandwidth requirement is very low. Performance evaluation of our method on challenging video sequences with frequent and severe occlusions shows that the accuracy of a baseline tracker is considerably improved. We also report the performance comparison to state of the art trackers in which our method outperforms.

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