Seeing Crucial Parts: Vehicle Model Verification via a Discriminative Representation Model
Liqian Liang, Congyan Lang, Zun Li, Jian Zhao, Tao Wang, Songhe Feng · ACM Transactions on Multimedia Computing Communications and Applications · 2022
Widely used surveillance cameras have promoted large amounts of street scene data, which contains one important but long-neglected object: the vehicle. Here we focus on the challenging problem of vehicle model verification. Most previous works usually employ global features (e.g., fully connected features) to further perform vehicle-level deep metric learning (e.g., triplet-based network). However, we argue that it is noteworthy to investigate the distinctiveness of local features and consider vehicle-part-level metric learning by reducing the intra-class variance as much as possible. In this article, we introduce a simple yet powerful deep model—the enforced intra-class alignment network (EIA-Net)—which can learn a more discriminative image representation by localizing key vehicle parts and jointly incorporating two distance metrics: vehicle-level embedding and vehicle-part-sensitive embedding. For learning features, we propose an effective feature extraction module that is composed of two components: the regional proposal network (RPN)-based network and part-based CNN. The RPN is used to define key vehicle regions and aggregate local features on these regions, whereas part-based CNN offers supplementary global features for the RPN-based network. The fusion features learned by feature extraction module are cast into the deep metric learning module. Especially, we derived an enforced intra-class alignment loss by re-utilizing key vehicle part information to enhance reducing intra-class variance. Furthermore, we modify the coupled cluster loss to model the vehicle-level embedding by enlarging the inter-class variance while shortening intra-class variance. Extensive experiments over benchmark datasets VehicleID and CompCars have shown that the proposed EIA-Net significantly outperforms the state-of-the-art approaches for vehicle model verification. Furthermore, we also conduct comprehensive experiments on vehicle re-identification datasets (i.e., VehicleID and VeRi776) to validate the generalization ability effectiveness of our proposed method.