Online Robust Non-negative Dictionary Learning for Visual Tracking
Online Robust Non-negative Dictionary Learning for Visual Tracking
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DOI：
10.1109/iccv.2013.87
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
Naiyan Wang;Jingdong Wang;D. Yeung
中科院分区：
文献类型：
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作者：
Naiyan Wang;Jingdong Wang;D. Yeung
研究了视频序列中的视觉跟踪问题，提出了一种基于粒子滤波的鲁棒稀疏跟踪器。特别是，我们提出了一个在线的强大的非负字典学习算法更新的对象模板，使每个学习的模板可以捕捉到跟踪对象的一个独特的方面。这种方法的另一个吸引人的特性是它可以自动检测和拒绝遮挡和杂乱的背景在一个原则性的方式。此外，我们提出了一种使用Huber损失函数的新粒子表示公式。其优点是，它可以产生鲁棒的估计，而不使用平凡的模板采用以前的稀疏跟踪器，导致更快的计算。我们还揭示了这个新的配方和以前的使用平凡的模板之间的等价性。所提出的跟踪器的经验比较与一些具有挑战性的视频序列的国家的最先进的跟踪器。定量和定性的比较表明，我们提出的跟踪器是上级和更稳定。
This paper studies the visual tracking problem in video sequences and presents a novel robust sparse tracker under the particle filter framework. In particular, we propose an online robust non-negative dictionary learning algorithm for updating the object templates so that each learned template can capture a distinctive aspect of the tracked object. Another appealing property of this approach is that it can automatically detect and reject the occlusion and cluttered background in a principled way. In addition, we propose a new particle representation formulation using the Huber loss function. The advantage is that it can yield robust estimation without using trivial templates adopted by previous sparse trackers, leading to faster computation. We also reveal the equivalence between this new formulation and the previous one which uses trivial templates. The proposed tracker is empirically compared with state-of-the-art trackers on some challenging video sequences. Both quantitative and qualitative comparisons show that our proposed tracker is superior and more stable.