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An efficient global representation constrained by Angular Triplet loss for vehicle re-identification

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Abstract

Vehicle re-identification is becoming an increasingly important problem in modern intelligent transportation systems. Substantial results have been achieved with methods based on deep metric learning. Most of the previous works tend to design complicated neural network models or utilize extra information. In this work, we introduce a simple Angular Triplet loss on the basis of analysis of different feature representations constrained by softmax loss and triplet loss. A batch normalization layer with zero bias is adopted to pass through the embedded feature before loss calculation. Then, triplet loss is calculated in cosine metric space instead of Euclidean space. In this way, triplet loss can cooperate with softmax consistently. By unifying the metric space of these two types of losses, the proposed method achieves 77.3% and 95.9% in rank-1 on VehicleID and VeRi-776 datasets, respectively. With only global features utilized, the proposed model can be seen as an effective baseline for vehicle re-identification task.

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Acknowledgements

This research was funded by the National Natural Science Foundation of China under Grant 61633019, the Science Foundation of Chinese Aerospace Industry under Grant JCKY2018204B053 and the Autonomous Research Project of the State Key Laboratory of Industrial Control Technology, China (Grant No. ICT1917).

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Authors and Affiliations

  1. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China

    Jianyang Gu, Wei Jiang & Hao Luo

  2. Beijing Electro-mechanical Engineering Institute, Beijing, 100074, China

    Hongyan Yu

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  1. Jianyang Gu
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  2. Wei Jiang
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  3. Hao Luo
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Correspondence to Wei Jiang.

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Gu, J., Jiang, W., Luo, H. et al. An efficient global representation constrained by Angular Triplet loss for vehicle re-identification. Pattern Anal Applic 24, 367–379 (2021). https://doi.org/10.1007/s10044-020-00900-w

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