Title: Real-time robust tracking with part-based and spatio-temporal context

Authors: Yanxia Wei; Zhen Jiang; Junfeng Xiao; Xinli Xu

Addresses: Department of Precision Mechanical Engineering, Shanghai University, Shanghai 200444, China; School of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, Shandong, China ' Department of Precision Mechanical Engineering, Shanghai University, Shanghai 200444, China ' Department of Mechanical and Materials Engineering, The University of Western Ontario, London, Ontario, Canada ' School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, Shandong, China

Abstract: Owing to the significant and excellent performance of correlation filter in the aspect of computation convenience, correlation filters based trackers have become increasingly popular in the visual object tracking community. However, complete or partial occlusion is one of the major factors that seriously impact the tracking performance in visual tracking. To address this issue, we propose a novel tracking algorithm that perfectly integrates the results from the global correlation and local correlation filters for estimating the more accurate position of target. Then, we introduce the occlusion detection mechanism to eliminate the occlusion impact on the final position of object. In addition, our proposed tracker employs the spatial geometric constraints among the global object and local patches of object for preserving the structure integration of object. For verifying our method, we conduct extensive qualitative and quantitative experiments on challenging benchmark image sequences.

Keywords: tracking; correlation filter; occlusion; part-based strategy; spatial geometric constraint.

DOI: 10.1504/IJCAT.2021.114983

International Journal of Computer Applications in Technology, 2021 Vol.65 No.2, pp.97 - 109

Received: 27 Apr 2020
Accepted: 22 Jul 2020

Published online: 06 May 2021 *

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