Infrared Target Tracking Using Multi-Feature Joint Sparse Representation

Abstract

This paper proposed a novel sparse representation-based infrared target tracking method using multi-feature fusion to compensate for incomplete description of single feature. In the proposed method, we extract the intensity histogram and the data on-Local Entropy and Local Contrast Mean Difference information for feature representation. To combine various features, particle candidates and multiple feature descriptors of dictionary templates were encoded as kernel matrices. Every candidate particle was sparsely represented as a linear combination of a set of atom vectors of a dictionary. Then, the sparse target template representation model was efficiently constructed using a kernel trick method. Finally, under the framework of particle filter the weights of particles were determined by sparse coefficient reconstruction errors for tracking. For tracking, a template update strategy employing Adaptive Structural Local Sparse Appearance Tracking (ASLAS) was implemented. The experimental results on benchmark data set demonstrate the better performance over many existing ones.