Abstract:

Focusing on the issue of the great decrease in object tracking performance induced by complex background and occlusion, a visual tracking method is proposed. The object and background samples are first obtained according to the local correlation of the object in the temporal-spatial domain. In what follows, a dictionary learning model is established: the outliers generated by occlusion are captured by error terms, and the sparse encoding matrix and error matrix are punished by nonconvex minimax concave plus functions. In addition, inconsistent constraints are imposed on the dictionaries to improve the robustness and discriminability of dictionaries. Concerning the established nonconvex dictionary learning optimization issue, the majorization-minimization (MM) optimization method can be exploited to get better convergence. Finally, the reconstruction errors of the candidate object are computed from the learned discriminative dictionary to construct the object observation model, and after that, the object tracking is realized accurately based on the Bayesian inference framework. As compared to the existing state-of-the-art algorithms, simulation results show that the proposed algorithm can improve the accuracy and robustness of the object tracking significantly in complex environments.

Key words: visual tracking, sparse representation, dictionary learning, nonconvex optimization, Bayesian inference