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Moving object tracking in video surveillance using YOLOv3 and MeanShift
Author(s): Wei Lei; Dongjun Huang; Xiwen Cui
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Paper Abstract

Video surveillance is widely used and plays a huge role in society. Due to surveillance videos are often continuously produced, using these videos to track objects is a challenge for conventional moving object tracking methods. In this paper, in order to deal with the fast moving object and the problem of target occlusion, we propose an object tracking method based on YOLOv3 and MeanShift combined with Kalman filter aiming to improve the speed and accuracy of tracking. We use YOLOv3 to realize the detection and use the MeanShift combined with Kalman filter to track the target. The results of the experiment show that our method has achieved good results.

Paper Details

Date Published: 6 May 2019
PDF: 6 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106940 (6 May 2019); doi: 10.1117/12.2524252
Show Author Affiliations
Wei Lei, Central South Univ. (China)
Dongjun Huang, Central South Univ. (China)
Xiwen Cui, Central South Univ. (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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