Abstract
Person re-identificaton aims to retrieve interested person objects from the person image database in cross camera scene, which has a wide range of application value in the field of video surveillance and security. With the generation of massive monitoring data, the retrieval speed of person re-identificaton is required to be higher. Facing the problem of the slow retrieval speed of person re-identification in large-scale monitoring data, we propose a person re-identification method based on hash. When training hash mapping function, we innovatively adds a batch hash code learning (BHL) module in the network to generate hash code as supervision information, which contributes greatly in retaining similarity information between person image pairs. Although the retrieval speed gets improved due to the concise binary hash features from hash mapping function above, we still need to retain the high accuracy at the same time and a coarse-to-fine (CF) retrieval strategy is proposed. Experiments on two public person re-identification datasets, Market-1501 and DukeMTMC-ReID, show the effectiveness of the proposed method. Compared with the benchmark model, the performance of our method is only 0.5% lower in mAP, but higher by 0.3% in Rank-1 on Market-1501 dataset. And on DukeMTMC-ReID dataset, Rank-1 and mAP are only 0.3% and 0.4% lower. However, the retrieval speed of the two datasets is increased by 12 times and 16 times respectively.
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Acknowledgements
This work is supported by the project (NTUT-BJUT-110–05) of China.
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Song, B., Zhang, X., Zhu, T., Ren, B., Jia, M. (2021). Person Re-identification Based on Hash. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_17
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