Abstract：To seek a solution of tracking drift and object loss resulted from environmental interference and appearance change of the object, a object tracking algorithm via object saliency and adaptive background constraint is proposed. In the tracking framework of particle filter, the pixel characteristics of the object and the extended object are firstly weighted to construct the explicit model of the object according to the principle of Bayesian saliency. Next, the background around the object is considered adaptively by exploiting the saliency of the background. Finally, by judging the current appearance state of the object, the tracking result is obtained by taking advantage of the correlation between the object and the background. Matching error is reduced by the object saliency model, while tracking accuracy is improved by the adaptive constraint of background with occluded object and changed pose. The experimental results demonstrate the proposed method with stronger robustness and higher precision for object tracking.
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2017, Vol. 30 
