In most visual tracking tasks, the target is tracked by a bounding box given in the first\nframe. The complexity and redundancy of background information in the bounding box inevitably\nexist and affect tracking performance. To alleviate the influence of background, we propose a robust\nobject descriptor for visual tracking in this paper. First, we decompose the bounding box into\nnon-overlapping patches and extract the color and gradient histograms features for each patch.\nSecond, we adopt the minimum barrier distance (MBD) to calculate patch weights. Specifically,\nwe consider the boundary patches as the background seeds and calculate the MBD from each patch\nto the seed set as the weight of each patch since the weight calculated by MBD can represent the\ndifference between each patch and the background more effectively. Finally, we impose the weight\non the extracted feature to get the descriptor of each patch and then incorporate our MBD-based\ndescriptor into the structured support vector machine algorithm for tracking. Experiments on two\nbenchmark datasets demonstrate the effectiveness of the proposed approach.
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