Sparse representations have been applied to visual tracking by finding the best candidate region with minimal reconstruction error based on a set of target templates. However, most existing sparse trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidate regions, thereby making them less effective when similar objects appear at close proximity or under occlusion. In this paper, we propose a novel structural sparse representation, which not only exploits the intrinsic relationships among target candidate regions and local patches to learn their representations jointly, but also preserves the spatial structure among the local patches inside each target candidate region. For robust visual tracking, we take outliers resulting from occlusion and noise into account when searching for the best target region. Constructed within a Bayesian filtering framework, we show that the proposed algorithm accommodates most existing sparse trackers with respective merits. The formulated problem can be efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. Qualitative and quantitative evaluations on challenging benchmark datasets demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

中文翻译：

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鲁棒结构稀疏跟踪


通过基于一组目标模板寻找具有最小重建误差的最佳候选区域，稀疏表示已应用于视觉跟踪。然而，大多数现有的稀疏跟踪器仅考虑整体或局部表示，并没有充分利用目标候选区域之间和内部的内在结构，从而使得当相似对象出现在附近或被遮挡时，它们的效果较差。在本文中，我们提出了一种新颖的结构稀疏表示，它不仅利用目标候选区域和局部补丁之间的内在关系来共同学习它们的表示，而且还保留了每个目标候选区域内的局部补丁之间的空间结构。为了实现鲁棒的视觉跟踪，我们在搜索最佳目标区域时考虑了遮挡和噪声产生的异常值。在贝叶斯过滤框架内构建，我们表明所提出的算法能够适应大多数现有的稀疏跟踪器，并具有各自的优点。可以使用加速近端梯度方法有效地解决公式化的问题，该方法产生一系列封闭形式更新。对具有挑战性的基准数据集的定性和定量评估表明，所提出的跟踪算法相对于几种最先进的方法表现良好。