ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)||5|
|Section||Session 5: Information Processing Methods and Techniques|
|Published online||05 September 2017|
A Novel Fuzzy Data Association Approach for Visual Multi-object Tracking
ATR Key Laboratory, Shenzhen University, Shenzhen, Guang Dong, China
Shenzhen Chichen Cloud Net Co., LTD, Shenzhen, Guang Dong, China
Shenzhen Pan Go Microsystems Co.,LTD, Shenzhen, Guang Dong, China
Multiple object tracking (MOT) is one of the most important research areas in visual surveillance. However, some practical challenges remain to be overcome for implementing this technology, such as occlusion, missed detection, false detection, and abrupt camera motion. In this paper, to the visual multi-object tracking, a novel fuzzy data association algorithm is proposed. In order to incorporate expert experience into the proposed algorithm, a fuzzy inference system based on knowledge is designed, and the fuzzy membership degrees are used to substitute the association probabilities between the objects and observations. The experiment results on several public data sets show that the proposed algorithm has advantages over other state-of-the-art tracking algorithms in terms of efficiency.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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