Real-time visual tracking via Incremental Covariance Tensor Learning

Visual tracking is a challenging problem, as an object may change its appearance due to pose variations, illumination changes, and occlusions. Many algorithms have been proposed to update the target model using the large volume of available information during tracking, but at the cost of high computational complexity. To address this problem, we present a tracking approach that incrementally learns a low-dimensional covariance tensor representation, efficiently adapting online to appearance changes for each mode of the target with only ̃(1) computational complexity. Moreover, a weighting scheme is adopted to ensure less modeling power is expended fitting older observations. Both of these features contribute measurably to improving overall tracking performance. Tracking is then led by the Bayesian inference framework in which a particle filter is used to propagate sample distributions over time. With the help of integral images, our tracker achieves real-time performance. Extensive experiments demonstrate the effectiveness of the proposed tracking algorithm for the targets undergoing appearance variations.