Person re-identification, which aims at recognizing a person of interest across spatially disjoint camera views, is still a challenging task. Plenty of approaches emerge in recent years and some of them achieve good matching results. Given a probe image, we observe that the ranking results generated by different approaches differ from each other. Considering these conventional methods are reasonable, we propose an Adaptive Multi-Metric Fusion (AMMF) method which fuses the existing ranking results with query-specific weights. Experiments on two challenging databases, VIPeR and ETHZ, demonstrate that the proposed method achieves further performance improvement.
CITATION STYLE
Li, P., Liu, M., Gu, Y., Yao, L., & Yang, J. (2016). Adaptive multi-metric fusion for person re-identification. In Communications in Computer and Information Science (Vol. 662, pp. 258–267). Springer Verlag. https://doi.org/10.1007/978-981-10-3002-4_22
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