Heavy-Tailed Model for Visual Tracking via Robust Subspace Learning

Abstract

Video-based target tracking, in essence, deals with nonstationary image streams, which is a challenging task in computer vision, because there always appear many abnormal motions and severe occlusions among the objects in the complex real-world environment. In a statistical perspective, an abnormal motion often exhibits non-Gaussian heavy-tailed behavior, which may take a long time to simulate. Most existing algorithms are unable to tackle this issue. In order to address it, we propose a novel tracking algorithm(HIRPCA) based on a heavy-tailed framework, which can robustly capture the effect of abnormal motion. In addition, since the conventional PCA is susceptible to outlying measurements in the sense of the least mean squared error minimisation, we extend and improve the incremental and robust PCA to learn a better representation of object appearance in a low-dimensional subspace, contributing to improving the performance of tracking in complex environment, such as light condition, significant pose and scale variation, temporary complete occlusion and abnormal motion. A series of experimental results show the good performance of the proposed method.