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It is well known that achieving a robust visual tracking task is quite difficult, since it is easily interfered by scale variation, illumination variation, background clutter, occlusion and so on. Nevertheless, the performance of spatio-temporal context algorithm is remarkable, because the spatial context information of target is effectively employed in this algorithm. However, the capabilities of discriminate target and adjust to scale variation need to promote in complex scene. Furthermore, due to lack of an appropriate target model update strategy, its tracking capability also deteriorates. In the interest of tackling these problems, a multi-scale spatio-temporal context visual tracking algorithm based on target model adaptive update is proposed. Firstly, the histogram of oriented gradient features are adopted to describe the target and its surrounding regions to improve its discriminate ability. Secondly, a multi-scale estimation method is applied to predict the target scale variation. Then, the peak and the average peak to correlation energy of confidence map response are combined to evaluate the visual tracking status. When the status is stable, the current target is expressed in a low rank form and a CUR filter is learned. On the contrary, the CUR filter will be triggered to recapture the target. Finally, the experimental results demonstrate that the robustness of this algorithm is promoted obviously, and its overall performance is better than comparison algorithms.
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