Angle Metric Learning for Discriminative Features on Vehicle Re-Identification

Vehicle re-identification (Re-ID) facilitates the recognition and distinction of vehicles based on their visual characteristics in images or videos. However, accurately identifying a vehicle poses great challenges due to (i) the pronounced intra-instance variations encountered under varying lighting conditions such as day and night and (ii) the subtle inter-instance differences observed among similar vehicles. To address these challenges, the authors propose Angle Metric learning for Discriminative Features on vehicle Re-ID (termed as AMDF), which aims to maximise the variance between visual features of different classes while minimising the variance within the same class. AMDF comprehensively measures the angle and distance discrepancies between features. First, to mitigate the impact of lighting conditions on intra-class variation, the authors employ CycleGAN to generate images that simulate consistent lighting (either day or night), thereby standardising the conditions for distance measurement. Second, Swin Transformer was integrated to help generate more detailed features. At last, a novel angle metric loss based on cosine distance is proposed, which organically integrates angular metric and 2-norm metric, effectively maximising the decision boundary in angular space. Extensive experimental evaluations on three public datasets including VERI-776, VERI-Wild, and VEHICLEID, indicate that the method achieves state-of-the-art performance. The code of this project is released at https://github.com/ZnCu-0906/AMDF.