Kernel sparse tracking with compressive sensing

Online tracking is a challenging task to develop effective and efficient models to account for appearance change. However, most tracking algorithms only consider the holistic or local information and do not make full use of the appearance information. In this study, a novel tracking algorithm with sparse representation is proposed and the online classifier is learned to discriminate the target from the background. To reduce visual drift problem which is encountered in object tracking, a two-stage sparse representation method is proposed. The holistic information is used to estimate the initial tracking position, and the local information is used to determine the final tracking position. To improve the performance of the classifier and robustness of the algorithm, the kernel function is applied on the sparse representation. Moreover, the dimension of the target is reduced via compressive sensing. Besides, a simple and effective method for dictionary update is proposed. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed algorithm performs favourably against several state-of-the-art algorithms.