Steepest Descent For Efficient Covariance Tracking

Recent research has advocated the use of a covariance matrix of image features for tracking objects instead of the conventional histogram object representation models used in popular algorithms. In this paper we extend the covariance tracker and propose efficient algorithms with an emphasis on both improving the tracking accuracy and reducing the execution time. The algorithms are compared to a baseline covariance tracker and the popular histogram-based mean shift tracker. Quantitative evaluations on a publicly available dataset demonstrate the efficacy of the presented methods. Our algorithms obtain significant speedups factors up to 330 while reducing the tracking errors by 86-90% relative to the baseline approach.