Due to complex background and volatile object shape-appearance in image, the stability and accuracy of tracking algorithm is often disturbed and reduced. So how to accurately and robustly track object in object tracking application is a challenge topic at home and abroad. Built upon the methodologies of compressive tracking and spatio-temporal context, a simple yet robust object tracking method is proposed for solving the drift and occlusion problems in paper. It combines two existing classical ideas into a single framework: adaptive weighted idea and occlusion detection mechanism. In order to weaken interference problems of object background, object area is firstly partitioned into equal-sized sub-patches and the different weight related with location information is assigned for each patch; Then, for improving its robustness, Bhattacharyya distance is adopted to find out these samples with maximum discrimination; In addition, our proposed occlusion detection mechanism is for recapturing the tracked object when occlusion occurs. Many simulation experiments show that our proposed algorithm achieves more favorable performance than these existing state-of-the-art algorithms in handing various challenging infrared videos, especially occlusion and shape deformation.
An improved object tracking algorithm based on adaptive weighted strategy and occlusion detection mechanism
