Abstract
Feature fusion is proved to be very effective in the problem of person re-identification. Commonly the fusion feature can performs better than single features. In this paper, we address how to take advantages of different ranking results yield by using different features. We propose a cascade filter framework to alleviate the influence of the error determination when the ranking results of different features are not consistent. This method can make full use of the information provided by features. Extensive experiments on publicly available datasets show that the proposed method achieve favorable performance in terms of accuracy and efficiency.
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Acknowledgement
This work was supported in Science and Technology Commission of Shanghai Municipality (STCSM, Grant Nos. 15DZ1207403).
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Wang, X., Yang, H., Zhu, J., Chen, L., Huang, J. (2016). Person Re-identification Using Cascade Filter. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_43
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DOI: https://doi.org/10.1007/978-3-319-46257-8_43
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