Visual tracking based on incremental two-dimensional Maximum Margin Criterion

Abstract

This paper presents a novel visual tracking algorithm based on incremental two-dimensional Maximum Margin Criterion (2DMMC). 2DMMC is a promising discriminant criterion for image feature extraction and its specialities make it a good choice for visual tracking problem. The proposed approach uses the 2DMMC to learn a discriminant projection matrix that best separates the target from the background. The projection matrix is updated online by a incremental algorithm to handle the appearance variations of the target and background. A particle filter using an efficient likelihood function based on the projection matrix is used to predict the target location in each frame. Experiments show that the proposed tracking algorithm is able to track the target in complex scenarios.