Target tracking algorithm for pedestrians movement based on kernel-correlation filtering. Issue 10 (3rd October 2022)

• Record Type: Journal Article
• Authors: Du, Feng; Wang, Wan-Liang; Zhang, Zhi
• Is Part Of: Enterprise information systems. Volume 16:Issue 10/11(2022)
• Page Start: 1500
• Page End: 1514
• Publication Date: 2022-10-03
• Subjects: Kernel correlation filtering -- feature fusion -- scale change -- local occlusion -- model update; Information storage and retrieval systems -- Periodicals; Management information systems -- Periodicals; Electronic commerce -- Periodicals; 658.4038011
• DOI: 10.1080/17517575.2020.1755457
• Languages: English
• ISSNs: 1751-7575

Abstract

ABSTRACT: Objective: To reduce the effects of light changes, scale changes, local occlusion and other factors during target tracking, a kernel-correlation filtering (KCF) target tracking algorithm is introduced, which introduces the target block model. Results: Comparative experiments of multiple mainstream algorithms on multiple data sets. Experimental results show that the algorithm has the highest accuracy and success rate, which are 11.89% and 15.24% higher than the KCF algorithm, respectively, indicating that the algorithm proposed in this paper possesses a more sensitive response to changes in illumination. Among them, factors such as scale change and local occlusion are more robust.