Structural Low-Rank Tracking

Abstract

Visual object tracking is an important step for many computer vision applications. The task becomes very challenging when the target undergoes heavy occlusion, background clutters, and sudden illumination variations. Methods that incorporate sparse representation and low-rank assumptions on the target particles have achieved promising results. However, because of the lack of structural constraints, these methods show performance degradation when an object faces the aforementioned challenges. To alleviate these limitations, we propose a new structural low-rank modeling algorithm for robust object tracking. In the proposed algorithm, we enforce local spatial, global spatial and temporal appearance consistency among the particles in the low-rank subspace by constructing three graphs. The Laplacian matrices of these graphs are incorporated into the novel low-rank objective function which is solved using linearized alternating direction method with an adaptive penalty. Our proposed objective function jointly learns the spatial, global, and temporal structure of the target particles in consecutive frames and makes the proposed tracker consistent against many complex tracking scenarios. Results on two challenging benchmark datasets show the superiority of the proposed algorithm as compared to current state-of-the-art methods.