Joint compressive representation for multi-feature tracking

Abstract Multi-feature tracking is an interesting but challenging task. Many previous works just consider the fusion of different features from identical spectral image or identical features from different spectral images alone, which makes them be quite distinct from each other and be difficult to be integrated naturally. To address this problem, we propose a unified multi-feature tracking framework based on joint compressive sensing in this paper. Our framework can accept features extracted from identical spectral or different spectral images, and provide the flexibility to arbitrary add or remove feature. We formulate the multi-feature tracking as a joint minimization problem of multiple l1-norms with inequality constraint of multiple l2-norms, and derive a customized augmented Lagrange multiplier algorithm to solve the minimization problem, which provides efficient object tracking with both low computational burden and high accuracy. Besides, a collaborative template update scheme induced by sparsity concentration index is developed to keep track of the most representative templates throughout the tracking procedure. Experimental results on challenging visible and infrared sequences clearly demonstrate the robustness and effectiveness of the proposed method.