Target tracker with masked discriminative correlation filter. Issue 10 (9th June 2020)

• Record Type: Journal Article
• Authors: Liu, Hang; Li, Bodong
• Is Part Of: IET image processing. Volume 14:Issue 10(2020)
• Page Start: 2227
• Page End: 2234
• Publication Date: 2020-06-09
• Subjects: target tracking -- filtering theory -- correlation methods; target tracker -- masked discriminative correlation filter -- target tracking -- boundary effect -- fast‐motion scene -- background noise -- background clutter -- DCF filter; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0881
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The discriminative correlation filter (DCF) method is widely used in target tracking due to its real‐time performance. However, the computational efficiency of DCF results in boundary effect, which reduces the tracking accuracy in fast motion scene. Besides, background noise is always required to be carefully handled for they will cause trouble in scenes such as background clutter, occlusion, deformation etc. To address the two issues, this study proposes masked discriminative correlation filter, which uses mask to process DCF filter as well as target samples so as to suppress boundary effect and background noise. Experimental results on benchmark datasets show that the proposed tracker performs better than a series of benchmark trackers, and is superior to them in almost various scenes.