Tracking multiple targets based on min-cost network flows with detection in RGB-D data
by Mingxin Jiang; Zhenzhou Tang; Liming Chen
International Journal of Computational Science and Engineering (IJCSE), Vol. 15, No. 3/4, 2017

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.

Online publication date: Sun, 15-Oct-2017

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