Abstract
The human saliency feature has been increasingly used for person re-identification across non-overlapping cameras but is deficient in retaining the minor features of the salient region, thus resulting in matching accuracy decline. To address this challenge, we first propose to extract optimal regions from pedestrian images that contain high intra-region feature similarity. Subsequently, by computing the saliency of each region, we choose the most salient region, which contains not only saliency features but also minor features, to represent the corresponding pedestrian. Finally, by formulating the competitive matching as hypothesis in a matching game, we obtain the most suitable set of matching by iteratively computing the payoff of each hypothesis. We evaluate our scheme on three widely used public datasets, and experimental results verify the advantage of our proposed algorithm, which outperforms previous representative methods with a matching ratio of 10.8%.












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Acknowledgements
This work is supported by the National Natural Science Foundation of China under Grant No. 61572261 and 61702284, the Natural Science Foundation of Jiangsu Province under Grant No. BK20150868, the China Postdoctoral Science Foundation funded project under Grant No. 2014M551635, the NUPTSF under Grant No. NY214013, the Jiangsu Planned Projects for Postdoctoral Research Funds under Grant No.1302085B and 1701165C, and the Major Scientic Research Project of Higher Learning Institution of Henan Province under Grant No. 18A520022.
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Li, T., Sun, L., Han, C. et al. Person re-identification using salient region matching game. Multimed Tools Appl 77, 21393–21415 (2018). https://doi.org/10.1007/s11042-017-5541-9
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DOI: https://doi.org/10.1007/s11042-017-5541-9