Stripe-based and Attribute-aware Network: A Two-Branch Deep Model for Vehicle Re-identification

TL;DR

This paper introduces a novel two-branch deep neural network that combines stripe-based part features and attribute-aware global features to improve vehicle re-identification accuracy, effectively distinguishing similar vehicles with different attributes.

Contribution

The paper proposes a new two-branch deep model, SAN, that adaptively extracts discriminative features for vehicle Re-ID by combining part-level and attribute-aware global features.

Findings

Outperforms state-of-the-art methods on VehicleID and VeRi datasets.

Effectively distinguishes vehicles with similar appearances using attribute supervision.

Achieves higher re-identification accuracy by combining part and global features.

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, we propose a novel two-branch stripe-based and attribute-aware deep convolutional neural network (SAN) to learn the efficient feature embedding for vehicle Re-ID task. The two-branch neural network, consisting of stripe-based branch and attribute-aware branches, can adaptively extract the discriminative features from the visual appearance of vehicles. A 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 not only can separate vehicles with different attributes but also distinguish vehicle identities with the same attributes. The extensive experiments on both VehicleID and VeRi databases show that the proposed SAN method outperforms other state-of-the-art vehicle Re-ID approaches.