Minimum Barrier Distance-Based Object Descriptor for Visual Tracking

Abstract

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

Low-Rank Multi-Channel Features for Robust Visual Object Tracking

“…In SRDCFdecon [40] adaptive decontamination is used which adaptively learns the reliability of each training sample and eliminates the influence of contaminated ones. The minimum barrier distance (MBD) [41] is developed to mitigate the impact of background on the tracker accuracy. The MBD consider the dissimilarity value to weight the extracted feature at each target position.…”