Roubust Visual Object Tracking Using Covariance Features In Quasi-Monte Carlo Filter

Abstract

Abstract Image covariance features, enabled with efficient fusion of several different types of image features without any weighting or normalization, have low dimensions. The covariance-based trackers are robust and versatile with a modest computational cost. This paper investigates an object tracking algorithm using a sequential quasi-Monte Carlo (SQMC) filter combined with covariance features. The covariance features are used not only to model target appearance, but also to model background. The dissimilarity of target and background is integrated in the SQMC filter as an additional measurement for the particle weight. A target model update strategy using the element of Riemannian geometry is proposed for the variation of the target appearance. Comparison experiments are conducted on several image sequences, and the results show that the proposed algorithm can successfully track the object in the presence of appearance changes, cluttered background and even severe occlusions.