This paper proposes a target tracking method called Incremental Self-Updating Visual Tracking for robot platforms. Our tracker treats the tracking problem as a binary classification: the target and the background. The greyscale, HOG and LBP features are used in this work to represent the target and are integrated into a particle filter framework. To track the target over long time sequences, the tracker has to update its model to follow the most recent target. In order to deal with the problems of calculation waste and lack of model-updating strategy with the traditional methods, an intelligent and effective online self-updating strategy is devised to choose the optimal update opportunity. The strategy of updating the appearance model can be achieved based on the change in the discriminative capability between the current frame and the previous updated frame. By adjusting the update step adaptively, severe waste of calculation time for needless updates can be avoided while keeping the stability of the model. Moreover, the appearance model can be kept away from serious drift problems when the target undergoes temporary occlusion. The experimental results show that the proposed tracker can achieve robust and efficient performance in several benchmark-challenging video sequences with various complex environment changes in posture, scale, illumination and occlusion.
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