awesome-vehicle-re-identification

collection of dataset&paper&code on Vehicle Re-Identification

dataset

• VeRi-776 [Project] [paper]
• PKU-VehicleID [Project] [pdf]
• PKU-VD [Project] [pdf]
• VehicleReId [Project] [pdf]
• PKU-Vehicle[Project] [pdf]
• CompCars[Project] [pdf]

paper

2017 & before

1. Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identification

   • Wang Z, Tang L, Liu X, et al. Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 379-387. [pdf]

2. Learning Deep Neural Networks for Vehicle Re-ID With Visual-Spatio-Temporal Path Proposals

   • Shen Y, Xiao T, Li H, et al. Learning Deep Neural Networks for Vehicle Re-ID With Visual-Spatio-Temporal Path Proposals[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 1900-1909.[pdf] VeRi: mAP 58.27, top1: 83.49, top5: 90.04; siamese net, tripple loss, chain MRF. Useful!!!

3. Vehicle Re-Identification for Automatic Video Traffic Surveillance

   • Zapletal D, Herout A. Vehicle re-identification for automatic video traffic surveillance[C]//Computer Vision and Pattern Recognition Workshops (CVPRW), 2016 IEEE Conference on. IEEE, 2016: 1568-1574.[pdf]

4. Exploiting Multi-Grain Ranking Constraints for Precisely Searching Visually-similar Vehicles

   • Yan K, Tian Y, Wang Y, et al. Exploiting Multi-Grain Ranking Constraints for Precisely Searching Visually-similar Vehicles[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 562-570.[pdf]

5. Deep Relative Distance Learning- Tell the Difference Between Similar Vehicles

   • Liu H, Tian Y, Yang Y, et al. Deep relative distance learning: Tell the difference between similar vehicles[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 2167-2175.[pdf]

6. A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance

   • Liu X, Liu W, Mei T, et al. A deep learning-based approach to progressive vehicle re-identification for urban surveillance[C]//European Conference on Computer Vision. Springer, Cham, 2016: 869-884.[paper]

7. Large-Scale Vehicle Re-Identification in Urban Surveillance Videos

   • Liu X, Liu W, Ma H, et al. Large-scale vehicle re-identification in urban surveillance videos[C]//Multimedia and Expo (ICME), 2016 IEEE International Conference on. IEEE, 2016: 1-6.[paper]

2018

8. PROVID- Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance

   • Liu X, Liu W, Mei T, et al. PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance[J]. IEEE Transactions on Multimedia, 2018, 20(3): 645-658.[paper]

9. Group Sensitive Triplet Embedding for Vehicle Re-identification

   • Bai Y, Lou Y, Gao F, et al. Group Sensitive Triplet Embedding for Vehicle Re-identification[J]. IEEE Transactions on Multimedia, 2018.[paper] VERI766: MAP 59.47 HIT1: 96.24 HIT5: 98.97; COMPCARS: MAP: =0.402 1: 0.769; VEHICLEID: small: 0.754, medium: 0.743, large: 0.724. Group with K-Means to handle intra-class variance. Useful!!!

10. Improving triplet-wise training of convolutional neural network for vehicle re-identification

    • Zhang Y, Liu D, Zha Z J. Improving triplet-wise training of convolutional neural network for vehicle re-identification[C]//Multimedia and Expo (ICME), 2017 IEEE International Conference on. IEEE, 2017: 1386-1391.[paper]

11. Multi-modal metric learning for vehicle re-identification in traffic surveillance environment

    • Tang Y, Wu D, Jin Z, et al. Multi-modal metric learning for vehicle re-identification in traffic surveillance environment[C]//Image Processing (ICIP), 2017 IEEE International Conference on. IEEE, 2017: 2254-2258.[paper]

12. Vehicle re-identification by fusing multiple deep neural networks

    • Cui C, Sang N, Gao C, et al. Vehicle re-identification by fusing multiple deep neural networks[C]//Image Processing Theory, Tools and Applications (IPTA), 2017 Seventh International Conference on. IEEE, 2017: 1-6.[paper]

13. Deep hashing with multi-task learning for large-scale instance-level vehicle search

    • Liang D, Yan K, Wang Y, et al. Deep hashing with multi-task learning for large-scale instance-level vehicle search[C]//Multimedia & Expo Workshops (ICMEW), 2017 IEEE International Conference on. IEEE, 2017: 192-197.[paper]

14. Unsupervised Vehicle Re-Identification using Triplet Networks

    • Antonio Marin-Reyes P, Palazzi A, Bergamini L, et al. Unsupervised Vehicle Re-Identification Using Triplet Networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2018: 166-171. [paper]

15. Vehicle Re-Identification with the Space-Time Prior

    • Wu C W, Liu C T, Chiang C E, et al. Vehicle re-identification with the space-time prior[C]//CVPR Workshop (CVPRW) on the AI City Challenge. 2018. [paper][code]mAP: 53.35; R1: 82.06%.

16. Viewpoint-aware Attentive Multi-view Inference for Vehicle Re-identification

    • Zhou, Y., & Shao, L. (2018). Aware Attentive Multi-View Inference for Vehicle Re-Identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6489-6498).[paper]

17. Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification.

    • [paper][code]VeRi: mAP:56.80; R1: 74.79%; VehicleID: test800: R1: 83.8%.(ACCV2018)

18. RAM: A Region-Aware Deep Model for Vehicle Re-Identification

    • [paper] [code] (ICME2018)VeRi: mAP: 0.615, R1: 0.886; VehicleID: testsmall: 0.752.

2019

19. Attributes Guided Feature Learning for Vehicle Re-identification
    • [paper] [code] GAN for data genneration. Ensemble of color, class, direction. No use.
20. A unified neural network for object detection, multiple object tracking and vehicle re-identification
    • [paper] Concat two neighbor frame or camera. Use tripple loss to find similar vihecles in this concated image. However, it is no use for continuous frames.
21. A survey of advances in vision-based vehicle re-identification
    • [paper] Gste > vstm = nufact = oim
22. A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification
    • [paper] [code] Use key point to help Re-id. This code only has key point part. VeRi-776: mAP: 66.35, t1: 90.17, t5: 94.34; VehicleID: small: t1: 74.69, t2: 93.82, medium t1: 68.62, t2: 89.95; large: t1: 63.54, t5: 85.64;
23. Variational Representation Learning for Vehicle Re-Identification
    • [paper] No code. Result is not good. Veri: mAP: 59.18, t1: 88.08, t5 94.63; VehicleID: small: t1: 73.37, t5: 85.52. [Website contains many new Re-Id papers]