Structured and Consistent Multi-Layer Multi-Kernel Subtask Correction Filter Tracker

Some multi-task correlation filter trackers achieve the top-ranked performance in terms of accuracy and robustness. However, they directly fuse multiple types of features into a single kernel space. This operation fails to fully explore the discriminative strength and diversity of different features, and also ignores the structured correspondence of different tasks. To solve these issues, we propose a structured multi-kernel subtask correlation filter tracker with temporal-spatial consistency, which enjoys the merits of both layered multi-kernel subtask learning and structured correlation filter. Specifically, we firstly assign one kernel space to each channel feature. Multi-channel features correspond to multi-kernel spaces to boost their powerful discriminability. And then, we divide the target into multi-layer patches with different sizes, and regard the correlation filter trace of each patch with one channel feature as a subtask. In the following, we incorporate globally and locally structured correlation filters into a unified multi-kernel subtask particle tracking framework. The global and local subtasks complement and enhance each other with similar motion model. The proposed tracker not only exploits the cooperation and complementarity of layered multi-kernel subtask correlation filters, but also mines the underlying geometric structure of global subtasks, and the inner spatial locality correspondences of local subtasks inside the target. This operation is achieved by dual group sparsity regularized terms with mixed-norm l
_p,q
, which decomposes the multi-kernel subtask filter matrix into two collaborative components. They correspond to the adaptive filter feature selection and outlier subtask detection, respectively. Besides, the developed tracking model maintains the temporal coherence and spatial consistency of multi-layer subtask filters via the smooth regularizer. Finally, the tracking formulation is optimized by the accelerated proximal gradient approach (APG). Encouraging analyses on six benchmark datasets, verify the favorable effectiveness and robustness of our method against state-of-the-art trackers.