cmFighting / awesome-vehicle-re-identification

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

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]
• Vehicle-1M[Project] [pdf]
• VRIC [Project] [pdf]

paper

CVPR

1. 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]

2. 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]

3. 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. [pdf]

4. 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. [pdf]

5. 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).[pdf]

6. Single-camera and inter-camera vehicle tracking and 3D speed estimation based on fusion of visual and semantic features

   • Tang, Z. et al. (2018). NVIDIA AI City Challenge Workshop at CVPR 2018 [paper]

ICCV

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]

3. 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]

ECCV

1. 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]

IJCAI

1. Fast vehicle identification via ranked semantic sampling based embedding
   • Zheng F, Miao X, Huang H. Fast vehicle identification via ranked semantic sampling based embedding[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. AAAI Press, 2018: 3697-3703.[pdf]

PR

1. VR-PROUD: Vehicle Re-identification using PROgressive Unsupervised Deep architecture
   • Bashir R M S, Shahzad M, Fraz M M. VR-PROUD: Vehicle Re-identification Using PROgressive Unsupervised Deep Architecture[J]. Pattern Recognition, 2019.[paper]

IP

1. Vehicle re-identification by deep hidden multi-view inference

   • Zhou Y, Liu L, Shao L. Vehicle re-identification by deep hidden multi-view inference[J]. IEEE Transactions on Image Processing, 2018, 27(7): 3275-3287.[paper]

2. Embedding Adversarial Learning for Vehicle Re-Identification

   • Lou Y, Bai Y, Liu J, et al. Embedding Adversarial Learning for Vehicle Re-Identification[J]. IEEE Transactions on Image Processing, 2019.[paper]

ICME

1. 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]

2. 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]

3. 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]

4. Ram: a region-aware deep model for vehicle re-identification

   • Liu X, Zhang S, Huang Q, et al. Ram: a region-aware deep model for vehicle re-identification[C]//2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2018: 1-6.[Paper]

AAAI

1. Learning Coarse-to-Fine Structured Feature Embedding for Vehicle Re-Identification
   • Guo H, Zhao C, Liu Z, et al. Learning Coarse-to-Fine Structured Feature Embedding for Vehicle Re-Identification[C]//Thirty-Second AAAI Conference on Artificial Intelligence. 2018.[pdf]

MM

1. 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]

2. 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]

ACMMM

1. VP-ReID: vehicle and person re-identification system
   • Wei L, Liu X, Li J, et al. VP-ReID: vehicle and person re-identification system[C]//Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval. ACM, 2018: 501-504.[paper]

Others

1. 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]

2. 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]