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Global attention-assisted representation learning for vehicle re-identification

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Abstract

Like pedestrian re-identification, vehicle re-identification (re-id) is an important part of building smart cities, and its purpose is to identify the same vehicle in vehicle images captured by multiple cameras. Vehicle re-id is more challenging than pedestrian re-id because many vehicles have similar colors and shapes, and their visual differences are usually very subtle. Existing vehicle re-id methods often rely on additional, expensive annotations to distinguish different vehicles. In contrast, we propose a two-branch network based on global attention mechanisms (MultiAttention-Net), which distinguishes subtle differences through adaptive learning. We introduce a global attention mechanism to highlight the differences between similar vehicles; however, compared with global appearance features, local features are more discriminant. Therefore, we propose combining global and local features to train the network to further improve the performance of vehicle re-id. During testing, only global features are used to measure the similarity between vehicle images. The experimental results show that the proposed MultiAttention-Net re-id method performs well on the challenging VeRi and VehicleID datasets.

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  1. https://github.com/slpluan/vehicle_reid

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, 610000, China

    Liping Song, Xin Zhou & Yuanyuan Chen

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  1. Liping Song
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  2. Xin Zhou
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  3. Yuanyuan Chen
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Correspondence to Yuanyuan Chen.

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This work was supported by Department of Science and Technology of Sichuan Province, China (Grant Nos. 20ZDYF2060 and 2021YFQ0010)

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Song, L., Zhou, X. & Chen, Y. Global attention-assisted representation learning for vehicle re-identification. SIViP 16, 807–815 (2022). https://doi.org/10.1007/s11760-021-02021-1

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