Abstract
People often go together in groups, and group re-identification (G-ReID) is an important but less researched topic. The goal of G-ReID is to find a group of people under different surveillance camera perspectives. It not only faces the same challenge with traditional ReID, but also involves the changes in group layout and membership. To solve these problems, we propose a Single Feature Attention Learning Network (SFALN). The proposed network makes use of the abundant ReID datasets by transfer learning, and extracts effective feature information of the groups through attention mechanism. Experimental results on the public dataset demonstrate the state-of-the-art effectiveness of our approach.
This project is supported by the NSFC(62076258).
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Liu, X., Yu, L., Lai, J. (2021). Group Re-Identification Based on Single Feature Attention Learning Network (SFALN). In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13019. Springer, Cham. https://doi.org/10.1007/978-3-030-88004-0_45
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DOI: https://doi.org/10.1007/978-3-030-88004-0_45
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