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Visual tracking by proto-objects
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AI
DOI：10.1016/j.patcog.2013.01.020.png)
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摘要
En 中文
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. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Tracking
Proto-object
Saliency
Gibbs sampling
Bayesian
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
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