Title: Tracking multiple targets based on min-cost network flows with detection in RGB-D data

Authors: Mingxin Jiang; Zhenzhou Tang; Liming Chen

Addresses: Faculty of Electronic and Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, 223003, China ' College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, 325035, China ' School of Information and Communication Engineering, Dalian Nationalities University, Dalian, Liaoning, 116600, China

Abstract: Visual multi-target tracking technology is a challenging problem in computer vision. This study proposes a novel approach for multi-target tracking based on min-cost network flows in RGB-D data with tracking-by-detection scheme. Firstly, the moving objects are detected by fusing RGB information and depth information. Then, we formulate the multi-target tracking problem as a maximum a posteriori (MAP) estimation problem with specific constraints, and the problem is converted into a cost-flow network. Finally, using a min-cost flow algorithm, we can obtain the tracking results. Extensive experimental results show that the proposed algorithm greatly improves the robustness and accuracy and outperforms the state-of-the-art significantly.

Keywords: combined multi-target detection; min-cost network flows; maximum a posteriori; MAP; RGB-D sensor.

DOI: 10.1504/IJCSE.2017.087414

International Journal of Computational Science and Engineering, 2017 Vol.15 No.3/4, pp.330 - 339

Received: 13 Apr 2016
Accepted: 27 Aug 2016
Published online: 15 Oct 2017 *

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