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Pattern Analysis and Applications

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23.06.2020 | Industrial and commercial application

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

verfasst von: Jianyang Gu, Wei Jiang, Hao Luo, Hongyan Yu

Erschienen in: Pattern Analysis and Applications | Ausgabe 1/2021

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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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Titel: An efficient global representation constrained by Angular Triplet loss for vehicle re-identification
verfasst von: Jianyang Gu
Wei Jiang
Hao Luo
Hongyan Yu
Publikationsdatum: 23.06.2020
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 1/2021
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-020-00900-w

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