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Online Visual Multiple Target Tracking by Intuitionistic Fuzzy Data Association

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Abstract

In this paper, a novel frame-by-frame data association algorithm based on intuitionistic fuzzy sets is proposed for online visual multiple target tracking. In the proposed algorithm, the association costs between targets and measurements are replaced by the intuitionistic fuzzy membership degrees which are obtained by a modified intuitionistic fuzzy c-means clustering algorithm. In addition, in order to mine useful information from the uncertain measurements, a new intuitionistic index is defined and the intuitionistic fuzzy point operator is applied to extract valuable information from the intuitionistic index. Experiments with challenging public datasets demonstrate that the proposed visual tracking algorithm improves tracking performance compared to other algorithms.

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Acknowledgments

The authors would like to thank the editor and all anonymous reviewers for their valuable comments. This work was supported by the National Natural Science Foundation of China (61301074, 61271107), Natural Science Foundation of the Guangdong Province of China (S2012010009417), Science and Technology Program of Shenzhen (No. JCYJ20140418095735618), and Defense Advance Research Fund Project (91400C800501140C80340).

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Correspondence to Li Liang-qun.

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Jun, L., Wei-xin, X. & Liang-qun, L. Online Visual Multiple Target Tracking by Intuitionistic Fuzzy Data Association. Int. J. Fuzzy Syst. 19, 355–366 (2017). https://doi.org/10.1007/s40815-016-0172-2

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