Robust tracking of multiple persons in real-time video

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/107225

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107225

Title:	Robust tracking of multiple persons in real-time video
Authors:	陳慶瀚;Chen, Ching-Han;Wang, Chien-Chun;Yan, Miao-Chun
Contributors:	資訊電機學院資訊工程學系
Keywords:	Algorithms;Analysis;Background noise;Computer Communication Networks;Computer Science;Data Structures and Information Theory;Image processing systems;Labeling;Mathematical models;Multimedia Information Systems;Optimization;Optimization techniques;Searching;Segmentation;Social behavior;Special Purpose and Application-Based Systems;Studies;Swarm intelligence;Tracking;Tracking (position);Tracking control systems
Date:	2016-12-01
Issue Date:	2026-04-23 14:00:39 (UTC+8)
Publisher:	Springer Netherlands;New York: Springer US
Abstract:	摘要： In this paper, we present a robust person tracking method that the particle swarm optimization (PSO) algorithm is used as the tracking strategy. The method is divided into two procedures: object/background segmentation and tracking. For object/background segmentation, we use the temporal differencing to detect the regions of interest. For tracking, the PSO algorithm is used for overcome the robustness problem in the high noisy background and multiple moving persons and/or under occlusion. The particles in PSO represent the position, width and height of the search window, and the fitness function is calculated by the distance of the color feature vector and the histogram intersection. When occluded, we add the motion vector plus the previous position of the tracking model. The particles fly around the search region to obtain an optimal match of the target. The experiments show that the proposed method can track the single person, multiple people even when occluded, and is more efficient and accurate than the conventional particle filter method. 其他題名： Multimed Tools Appl 出版者： New York: Springer US 出版日期： 2016-12-01 出處： Multimedia tools and applications, 2016-12, Vol.75 (23), p.16683-16697 資源來源： ABI/INFORM Collection 版權： Springer Science+Business Media New York 2016 版權： Multimedia Tools and Applications is a copyright of Springer, 2016. 識別號： ISSN: 1380-7501 識別號： EISSN: 1573-7721 識別號： DOI: 10.1007/s11042-016-3890-4
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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