Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification

• URL: http://arxiv.org/abs/2204.13323v1
• Date: Thu, 28 Apr 2022 07:46:03 GMT
• Title: Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification
• Authors: Huadong Li, Yuefeng Wang, Ying Wei, Lin Wang, Li Ge
• Abstract summary: Multiple challenges hamper the applications of vision-based vehicle Re-ID methods. The proposed DRA model can automatically extract the discriminative region features, which can distinguish similar vehicles. And the OVG model can generate multi-view features based on the input view features to reduce the impact of viewpoint mismatches.
• Score: 7.5366501970852955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vehicle re-identification (Re-ID) is urgently demanded to alleviate thepressure caused by the increasingly onerous task of urban traffic management. Multiple challenges hamper the applications of vision-based vehicle Re-ID methods: (1) The appearances of different vehicles of the same brand/model are often similar; However, (2) the appearances of the same vehicle differ significantly from different viewpoints. Previous methods mainly use manually annotated multi-attribute datasets to assist the network in getting detailed cues and in inferencing multi-view to improve the vehicle Re-ID performance. However, finely labeled vehicle datasets are usually unattainable in real application scenarios. Hence, we propose a Discriminative-Region Attention and Orthogonal-View Generation (DRA-OVG) model, which only requires identity (ID) labels to conquer the multiple challenges of vehicle Re-ID.The proposed DRA model can automatically extract the discriminative region features, which can distinguish similar vehicles. And the OVG model can generate multi-view features based on the input view features to reduce the impact of viewpoint mismatches. Finally, the distance between vehicle appearances is presented by the discriminative region features and multi-view features together. Therefore, the significance of pairwise distance measure between vehicles is enhanced in acomplete feature space. Extensive experiments substantiate the effectiveness of each proposed ingredient, and experimental results indicate that our approach achieves remarkable improvements over the state- of-the-art vehicle Re-ID methods on VehicleID and VeRi-776 datasets.

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