基于联合稀疏的鲁棒视觉跟踪

B. Bozorgtabar, Roland Göcke
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引用次数: 0

摘要

在本文中,我们提出了一种利用联合稀疏性模型的粒子滤波框架中的目标跟踪方法。基于多个动态更新的模板可以重构目标的观察,我们共同分析了单个回归框架和共享底层结构下粒子的表示。在我们的模型中,将两个凸正则化结合起来,以实现稀疏性并促进粒子之间的耦合信息。不同于以往的方法考虑粒子之间的模型共性或将它们视为独立的任务,我们同时考虑了结构诱导范数和离群检测范数。这样的公式被证明在处理各种类型的挑战方面更加灵活,包括遮挡和杂乱的背景。为了有效地推导出最优解,我们建议使用预条件共轭梯度方法,该方法在计算上对高维数据负担得起。此外,字典学习中还包含在线更新过程方案,使所提出的跟踪器不容易受到异常值的影响。在具有挑战性的视频序列上的实验证明了该方法在处理遮挡、姿态和光照变化方面的鲁棒性,并且在跟踪精度方面优于最先进的跟踪器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint sparsity-based robust visual tracking
In this paper, we propose a new object tracking in a particle filter framework utilising a joint sparsity-based model. Based on the observation that a target can be reconstructed from several templates that are updated dynamically, we jointly analyse the representation of the particles under a single regression framework and with the shared underlying structure. Two convex regularisations are combined and used in our model to enable sparsity as well as facilitate coupling information between particles. Unlike the previous methods that consider a model commonality between particles or regard them as independent tasks, we simultaneously take into account a structure inducing norm and an outlier detecting norm. Such a formulation is shown to be more flexible in terms of handling various types of challenges including occlusion and cluttered background. To derive the optimal solution efficiently, we propose to use a Preconditioned Conjugate Gradient method, which is computationally affordable for high-dimensional data. Furthermore, an online updating procedure scheme is included in the dictionary learning, which makes the proposed tracker less vulnerable to outliers. Experiments on challenging video sequences demonstrate the robustness of the proposed approach to handling occlusion, pose and illumination variation and outperform state-of-the-art trackers in tracking accuracy.
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