Robust Visual Tracking using 1 Minimization
Robust Visual Tracking using 1 Minimization
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发表时间：
2009
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通讯作者：
Xue Mei;Haibin Ling
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作者：
Xue Mei;Haibin Ling
在本文中，我们提出了一个强大的视觉跟踪方法铸造跟踪作为一个稀疏近似问题的粒子滤波框架。在这个框架中，遮挡，腐败和其他具有挑战性的问题是通过一组微不足道的模板无缝解决。具体地说，为了在新的帧找到跟踪目标，每个目标候选者稀疏地表示在目标模板和平凡模板所跨越的空间中。稀疏性是通过求解一个1-正则化最小二乘问题来实现的。然后将投影误差最小的候选者作为跟踪目标。之后，跟踪继续使用贝叶斯状态推断框架，其中粒子滤波器用于随时间传播样本分布。两个额外的组件进一步提高了我们的方法的鲁棒性：1）非负性约束，帮助过滤掉类似于反向强度模式中的跟踪目标的杂波，以及2）动态模板更新方案，在整个跟踪过程中跟踪最具代表性的模板。我们测试了五个具有挑战性的序列，涉及严重的闭塞，剧烈的照明变化，和大的姿态变化所提出的方法。所提出的方法表现出优异的性能与以前提出的跟踪器相比。
In this paper we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, corruption and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an � 1-regularized least squares problem. Then the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. Two additional components further improve the robustness of our approach: 1) the nonnegativity constraints that help filter out clutter that is similar to tracked targets in reversed intensity patterns, and 2) a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure. We test the proposed approach on five challenging sequences involving heavy occlusions, drastic illumination changes, and large pose variations. The proposed approach shows excellent performance in comparison with three previously proposed trackers.