Nonconvex dictionary learning based visual tracking method

Abstract

Focusing on the heavy decrease of object tracking performance induced by complex circumstances, an object tracking method based on nonconvex discriminative dictionary learning (NDDL) is proposed. Firstly, the object and background samples are acquired according to the temporal and spatial local correlation of objects. Since object and background samples have some common features, an inconsistent constraint is imposed on dictionaries to improve their robustness and discriminability. In what follows, a nonconvex minimax concave plus (MCP) function can be used to penalize sparse encoding matrices to avoid over-punishment via some convex relaxation methods. Based on the sparse representation (SR) theory, a NDDL model can be constructed, which can be tackled by majorization-minimization inexact augmented Lagrange multiplier (MM-IALM) optimization method to achieve better convergence. After obtaining the optimal discriminative dictionary, the reconstruction errors of all candidates are calculated to construct the object observation model. Finally, the object tracking is implemented accurately based on the Bayesian inference framework. Compared to the existing state-of-the-art trackers, simulation results show that the proposed tracker can improve the precision and success rate of the object tracking significantly in complex circumstances.