Abstract
In this study, we present a hybrid model that combines the advantages of the identification, verification and triplet models for person re-identification. Specifically, the proposed model simultaneously uses Online Instance Matching (OIM), verification and triplet losses to train the carefully designed network. Given a triplet images, the model can output the identities of the three input images and the similarity score as well as make the L-2 distance between the mismatched pair larger than the one between the matched pair. Experiments on two benchmark datasets (CUHK01 and Market-1501) show that the proposed method can achieve favorable accuracy while compared with other state of the art methods.
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Acknowledgements
This work was supported by the grants of the National Science Foundation of China, Nos. 61472280, 61672203, 61472173, 61572447, 61772357, 31571364, 61520106006, 61772370, 61702371 and 61672382, China Postdoctoral Science Foundation Grant, Nos. 2016M601646 & 2017M611619, and supported by “BAGUI Scholar” Program of Guangxi Zhuang Autonomous Region of China. De-Shuang Huang is the corresponding author of this paper.
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Wu, D. et al. (2018). A Hybrid Deep Model for Person Re-Identification. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_25
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