Accurate target tracking via Gaussian sparsity and locality-constrained coding in heavy occlusion

This paper presents a Gaussian sparse representation cooperative model for tracking a target in heavy occlusion video sequences by combining sparse coding and locality-constrained linear coding algorithms. Different from the usual method of using ℓ 1 -norm regularization term in the framework of particle filters to form the sparse collaborative appearance model (SCM), we employed the ℓ 1 -norm and ℓ 2 -norm to calculate feature selection, and then encoded the candidate samples to generate the sparse coefficients. Consequently, our method not only easily obtained sparse solutions but also reduced reconstruction error. Compared to state-of-the-art algorithms, our scheme achieved better performance in heavy occlusion video sequences for tracking a target. Extensive experiments on target tracking were carried out to show the results of our proposed algorithm compared with various other target tracking methods.