In this paper we present a novel visual tracking algorithm, in which object tracking is achieved by using subspace learning and Huber loss regularization in a particle filter framework. The changing appearance of tracked target is modeled by principle component analysis basis vectors and row group sparsity. This method takes advantage of the strengths of subspace representation and explicitly takes the underlying relationship between particle candidates into consideration in the tracker. The representation of each particle is learned via the multi-task sparse learning method. Huber loss function is employed to model the error between candidates and templates, yielding robust tracking. We utilize the alternating direction method of multipliers to solve the proposed representation model. In experiments we tested sixty representative video sequences that reflect the specific challenges of tracking and used both qualitative and quantitative metrics to evaluate the performance of our tracker. The experiment results demonstrated that the proposed tracking algorithm achieves superior performance compared to nine state-of-the-art tracking methods.

中文翻译：

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基于Huber损失函数的目标跟踪

在本文中，我们提出了一种新颖的视觉跟踪算法，其中通过在粒子滤波器框架中使用子空间学习和 Huber 损失正则化来实现对象跟踪。通过主成分分析基向量和行组稀疏性对跟踪目标的变化外观进行建模。该方法利用子空间表示的优势，并在跟踪器中明确考虑候选粒子之间的潜在关系。每个粒子的表示是通过多任务稀疏学习方法学习的。Huber 损失函数用于对候选者和模板之间的误差进行建模，从而产生鲁棒的跟踪。我们利用乘法器的交替方向方法来求解所提出的表示模型。在实验中，我们测试了六十个代表性视频序列，这些序列反映了跟踪的具体挑战，并使用定性和定量指标来评估跟踪器的性能。实验结果表明，与九种最先进的跟踪方法相比，所提出的跟踪算法具有优越的性能。