Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification
This work solves vehicle re-identification for urban traffic management, offering a method that reduces reliance on finely labeled datasets, though it is incremental as it builds on existing vision-based approaches.
The paper tackles vehicle re-identification by addressing challenges like similar appearances across vehicles and viewpoint variations, proposing a model that uses only identity labels to extract discriminative region features and generate multi-view features, achieving state-of-the-art improvements on VehicleID and VeRi-776 datasets.
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.