An improved object tracking algorithm based on adaptive weighted strategy and occlusion detection mechanism. (January 2021)

• Record Type: Journal Article
• Authors: Tian, Xiuyan; Li, Haifang; Deng, Hongxia
• Is Part Of: Journal of algorithms & computational technology. Volume 15(2021)
• Publication Date: 2021-01
• Subjects: Compressive tracking -- adaptive weight -- infrared object tracking -- maximum discrimination -- Bhattacharyya distance -- occlusion; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748302620973536
• Languages: English
• ISSNs: 1748-3018

Abstract

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.