Visual Tracking by Proto-Objects

Zhidong Li, Weihong Wang, Yang Wang, Fang Chen, Yi Wang

Pattern Recognition

In this paper, we propose a biologically inspired framework of visual tracking based on proto-objects. Given an image sequence, proto-objects are first detected by combining saliency map and topic model. Then the target is tracked based on spatial and saliency information of the proto-objects. In the proposed Bayesian approach, states of the target and proto-objects are jointly estimated over time. Gibbs sampling has been used to optimize the estimation during the tracking process. The proposed method robustly handles occlusion, distraction, and illumination change in the experiments. Experimental results also demonstrate that the proposed method outperforms the state-of-the-art methods in challenging tracking tasks.

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