Attributes Guided Feature Learning for Vehicle Re-Identification

Li, Hongchao; Lin, Xianmin; Zheng, Aihua; Li, Chenglong; Luo, Bin; He, Ran; Hussain, Amir

Abstract

Vehicle Re-ID has recently attracted enthusiastic attention due to its potential applications in smart city and urban surveillance. However, it suffers from large intra-class variation caused by view variations and illumination changes, and inter-class similarity especially for different identities with a similar appearance. To handle these issues, in this paper, we propose a novel deep network architecture, which guided by meaningful attributes including camera views, vehicle types and colors for vehicle Re-ID. In particular, our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes. For network training, we annotate the view labels on the VeRi-776 dataset. Note that one can directly adopt the pre-trained view (as well as type and color) subnetwork on the other datasets with only ID information, which demonstrates the generalization of our model. Extensive experiments on the benchmark datasets VeRi-776 and VehicleID suggest that the proposed approach achieves the promising performance and yields to a new state-of-the-art for vehicle Re-ID.

Citation

Li, H., Lin, X., Zheng, A., Li, C., Luo, B., He, R., & Hussain, A. (2022). Attributes Guided Feature Learning for Vehicle Re-Identification. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(5), 1211-1221. https://doi.org/10.1109/tetci.2021.3127906

• Journal Article Type: Article
• Acceptance Date: Nov 3, 2021
• Online Publication Date: Dec 1, 2021
• Publication Date: 2022-10
• Deposit Date: Sep 28, 2022
• Journal: IEEE Transactions on Emerging Topics in Computational Intelligence
• Print ISSN: 2471-285X
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Issue: 5
• Pages: 1211-1221
• DOI: https://doi.org/10.1109/tetci.2021.3127906
• Public URL: http://researchrepository.napier.ac.uk/Output/2922747