Robust Object Tracking via Key Patch Sparse Representation
提出一种基于关键块稀疏表示的跟踪方法，通过选择关键块并赋予贡献因子，减少部分遮挡和背景杂波对跟踪的干扰，在多个基准数据集上表现优于现有方法。
Many conventional computer vision object tracking methods are sensitive to partial occlusion and background clutter. This is because the partial occlusion or little background information may exist in the bounding box, which tends to cause the drift. To this end, in this paper, we propose a robust tracker based on key patch sparse representation (KPSR) to reduce the disturbance of partial occlusion or unavoidable background information. Specifically, KPSR first uses patch sparse representations to get the patch score of each patch. Second, KPSR proposes a selection criterion of key patch to judge the patches within the bounding box and select the key patch according to its location and occlusion case. Third, KPSR designs the corresponding contribution factor for the sampled patches to emphasize the contribution of the selected key patches. Comparing the KPSR with eight other contemporary tracking methods on 13 benchmark video data sets, the experimental results show that the KPSR tracker outperforms classical or state-of-the-art tracking methods in the presence of partial occlusion, background clutter, and illumination change.