Bayesian Matrix Factorization for Visual Tracking

Abstract

Visual tracking is an open issue in computer vision. Designing an robust tracking algorithm is a challenging problem due to complicated appearance variations, such as partial occlusion, illumination variations, fast motion and out-of-plane rotation. Some tracking algorithms use a set of templates to represent a target candidate. Such representations are not robust to drastic appearance variations. In order to alleviate the influence of appearance variations and outliers, we propose a novel tracking algorithm based on a robust Bayesian matrix factorization, where sample data is decomposed into a basis matrix and a coefficient matrix. The sample data is consist of previous tracking results. A column vector is stacked by a tracking result in a frame. A target candidate is represented by a linear combinations of the columns in the basis matrix. The matrix factorization can capture the correlation between different features in sample data. Also the appearance variations in tracking processing can be learnt in matrix factorization. Experimental results demonstrate the appearance models is effective and the proposed tracker in a particle filter tracking framework is robust to outliers and complicated appearance variations.