Stripe-based and attribute-aware network: a two-branch deep model for vehicle re-identification

Abstract Vehicle re-identification (Re-ID) has been attracting increasing interest in the field of computer vision due to the growing utilization of surveillance cameras in public security. However, vehicle Re-ID still suffers a similarity challenge, despite the efforts made to solve this problem. This challenge involves distinguishing different instances with nearly identical appearances. In this paper, a novel two-branch stripe-based and attribute-aware deep convolutional neural network (SAN) is proposed to learn the efficient feature embedding for a vehicle Re-ID task. The two-branch neural network, consisting of a stripe-based branch and an attribute-aware branch, can adaptively extract the discriminative features from the visual appearance of vehicles. Horizontal average pooling and dimension-reduced convolutional layers are inserted into the stripe-based branch to achieve part-level features. Meanwhile, the attribute-aware branch extracts the global feature under the supervision of vehicle attribute labels to separate the similar vehicle identities with different attribute annotations. Finally, the part-level and global features are concatenated together to form the final descriptor of the input image for vehicle Re-ID. The final descriptor can not only separate vehicles with different attributes but also distinguish vehicle identities with the same attributes. The extensive experiments on both the VehicleID and VeRi datasets show that the proposed SAN method outperforms other state-of-the-art vehicle Re-ID approaches.