Conjugate gradient algorithm for efficient covariance tracking with Jensen-Bregman LogDet metric

Region covariance descriptor that fuses multiple features compactly has proven to be very effective for visual tracking. While working effectively, the exhaustive global search strategy of covariance tracking is still inefficient, and there is much room for improvement. It may cause inconsecutive tracking trajectory and distraction. A suitable region similarity metric for covariance matching between the candidate object region and a given appearance template is of much importance. However, the computational burden of the metric, especially for large matrices under Riemannian space, may hinder its application in gradient-based algorithms. In this study, the authors propose an algorithm which, by minimising the metric function, exploits an efficient conjugate gradient method to iteratively search the best matched candidate, and determines the search step size by non-monotonic liner strategy. Then, an inferential reasoning in view of new efficient metric is derived for the gradient-based algorithm. The authors test the proposed tracking method on test baseline dataset. Both quantitative and qualitative results demonstrate the effectiveness of the proposed algorithm compared with other state-of-the-art methods.