# Vehicle_reID-Collection

**Repository Path**: Cloud-Rambler/Vehicle_reID-Collection

## Basic Information

- **Project Name**: Vehicle_reID-Collection
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-09-13
- **Last Updated**: 2021-10-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Vehicle Re-ID Collection

If you notice any result or the public code that has not been included in this page, please connect [Zhedong Zheng](mailto:zdzheng12@gmail.com) without hesitation to add the method. You are welcomed! 
or create pull request.

Priorities are given to papers whose codes are published.

## Code 
🏎️. The 1st Place Submission to AICity Challenge 2021 nlp re-id track (CVPR 2021 workshop) [[code]](https://github.com/ShuaiBai623/AIC2021-T5-CLV)[[paper]](https://github.com/layumi/NLP-AICity2021/blob/main/doc/CVPRW2021_NLP_AICity.pdf)

🚙: The 2nd Place Submission to AICity Challenge 2021 re-id track (CVPR 2021 workshop) [[code]](https://github.com/Xuanmeng-Zhang/AICITY2021-Track2)

:red_car:  The 1st Place Submission to AICity Challenge 2020 re-id track (CVPR 2020 workshop) [[code]](https://github.com/layumi/AICIty-reID-2020)
 [[paper]](https://github.com/layumi/AICIty-reID-2020/blob/master/paper.pdf)
 
 :helicopter:  Drone-based building re-id (ACM Multimedia 2020) [[code]](https://github.com/layumi/University1652-Baseline)  [[paper]](https://arxiv.org/abs/2002.12186)
 
 GPU-based Fast Re-Ranking [[code]](https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/GPU-Re-Ranking) [[paper]](https://arxiv.org/abs/2012.07620v2)

## Dataset
1. VeRi-776

    [project](https://github.com/VehicleReId/VeRidataset) [paper](https://link.springer.com/chapter/10.1007/978-3-319-46475-6_53)

49,357 images of 776 vehicles from 20 cameras. Like Market-1501 protocol.

The VeRi dataset is divided into a training subset containing 37,781 images of 576 subjects and a testing subset with 13,257 images of 200 subjects.then a query set containing 1,678 images of 200 subjects and a gallery including 11,579 image of 200 subjects are finally obtained.

2. PKU Vehicle-ID

    [project](https://pkuml.org/resources/pku-vehicleid.html) [pdf](http://openaccess.thecvf.com/content_cvpr_2016/papers/Liu_Deep_Relative_Distance_CVPR_2016_paper.pdf)

221,763 images of 2,627 vehicles. Only two camera views??

3. PKU-VD

    [project](https://pkuml.org/resources/pku-vds.html) [pdf](http://openaccess.thecvf.com/content_ICCV_2017/papers/Yan_Exploiting_Multi-Grain_Ranking_ICCV_2017_paper.pdf)

with attribute.

4. VehicleReID

    [project](https://medusa.fit.vutbr.cz/traffic/research-topics/detection-of-vehicles-and-datasets/vehicle-re-identification-for-automatic-video-traffic-surveillance-ats-cvpr-2016/) [pdf](http://openaccess.thecvf.com/content_cvpr_2016_workshops/w25/papers/Zapletal_Vehicle_Re-Identification_for_CVPR_2016_paper.pdf)

47,123 images from two cameras & lablled on pair.

5. PKU-Vehicle

    [project](http://59.110.216.11/html/) [paper](https://ieeexplore.ieee.org/abstract/document/8265213)

no ID lablled.

6. CompCars

    [project](http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html) [pdf](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Yang_A_Large-Scale_Car_2015_CVPR_paper.pdf) 

136,726 + 27,618 images of 1,716 cars with attributes. After crop,  136,713.

7. StanfordCars

    [project](http://ai.stanford.edu/~jkrause/cars/car_dataset.html) [pdf](http://ai.stanford.edu/~jkrause/papers/3drr13.pdf)

16,185 images of 196 classes.

8. Vehicle-1M

    [project](http://www.nlpr.ia.ac.cn/iva/homepage/jqwang/Vehicle1M.htm) [pdf](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16206/16270)
    
    
9. VERI-Wild 

[project](https://github.com/PKU-IMRE/VERI-Wild)


## Recent Papers
### **2020**
1. VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification **(TMM)**  [arXiv](https://arxiv.org/abs/2004.06305) [[中文介绍]](https://zhuanlan.zhihu.com/p/186905783)

2. Beyond the Parts: Learning Multi-view Cross-part Correlation for Vehicle Re-identification **(ACM MM)** [paper](http://xinchenliu.com/papers/2020_ACMMM_PCRNet.pdf) [code](https://github.com/lxc86739795/parsing_platform)

### **2019**
1. VR-PROUD: Vehicle Re-identification using PROgressive Unsupervised Deep architecture **(PR)** [paper](https://www.sciencedirect.com/science/article/abs/pii/S0031320319300147)

2. Embedding Adversarial Learning for Vehicle Re-Identification **(TIP)** [paper](https://ieeexplore.ieee.org/abstract/document/8653852)

3. Vehicle Re-Identification Using Quadruple Directional Deep Learning Features **(TITS)** [pdf](https://arxiv.org/pdf/1811.05163.pdf)

4. VehicleNet: Learning Robust Feature Representation for Vehicle Re-identification **（CVPR workshop）** [paper](http://openaccess.thecvf.com/content_CVPRW_2019/html/AI_City/Zheng_VehicleNet_Learning_Robust_Feature_Representation_for_Vehicle_Re-identification_CVPRW_2019_paper.html)

5. Part-regularized Near-duplicate Vehicle Re-identification **(CVPR)** [pdf](http://cvteam.net/papers/2019_CVPR_Part-regularized%20Near-duplicate%20Vehicle%20Re-identification.pdf)

### **2018**
1. Viewpoint-aware Attentive Multi-view Inference for Vehicle Re-identification **(CVPR)** [pdf](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Viewpoint-Aware_Attentive_Multi-View_CVPR_2018_paper.pdf)

2. Unsupervised Vehicle Re-Identification using Triplet Networks **(CVPR workshop)** [pdf](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w3/Marin-Reyes_Unsupervised_Vehicle_Re-Identification_CVPR_2018_paper.pdf)

3. Vehicle Re-Identification with the Space-Time Prior **(CVPR workshop)** [pdf](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w3/Wu_Vehicle_Re-Identification_With_CVPR_2018_paper.pdf)

4. Fast vehicle identification via ranked semantic sampling based embedding **(IJCAI)** [pdf](https://www.ijcai.org/proceedings/2018/0514.pdf)

5. Vehicle re-identification by deep hidden multi-view inference **(TIP)** [paper](https://ieeexplore.ieee.org/abstract/document/8325486)

6. Ram: a region-aware deep model for vehicle re-identification **(ICME)** [pdf](https://arxiv.org/pdf/1806.09283.pdf)

7. Learning Coarse-to-Fine Structured Feature Embedding for Vehicle Re-Identification **(AAAI)** [pdf](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16206/16270)

8. PROVID- Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance **(TMM)** [paper](https://ieeexplore.ieee.org/abstract/document/8036238)

9. Group Sensitive Triplet Embedding for Vehicle Re-identification **(TMM)** [paper](https://ieeexplore.ieee.org/abstract/document/8265213)

10. VP-ReID: vehicle and person re-identification system **(ACMMM)** [paper](https://dl.acm.org/citation.cfm?id=3206086)

11. Vehicle Re-Identification by Adversarial Bi-Directional LSTM Network **(WACV)** [paper](https://ieeexplore.ieee.org/abstract/document/8354181/)

12. Joint Semi-supervised Learning and Re-ranking for Vehicle Re-identification **ICPR** [paper](https://ieeexplore.ieee.org/abstract/document/8545584/)

13. Multi-Attribute Driven Vehicle Re-Identification with Spatial-Temporal Re-Ranking **ICIP** [paper](https://ieeexplore.ieee.org/abstract/document/8451776/)

14. Joint feature and similarity deep learning for vehicle re-identification **IEEE Access** [paper](https://ieeexplore.ieee.org/abstract/document/8424333/)
### **2017**
1. Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identification **(ICCV)** [pdf](http://openaccess.thecvf.com/content_ICCV_2017/papers/Wang_Orientation_Invariant_Feature_ICCV_2017_paper.pdf)

2. Learning Deep Neural Networks for Vehicle Re-ID With Visual-Spatio-Temporal Path Proposals **(ICCV)** [pdf](http://openaccess.thecvf.com/content_ICCV_2017/papers/Shen_Learning_Deep_Neural_ICCV_2017_paper.pdf)

3. Exploiting Multi-Grain Ranking Constraints for Precisely Searching Visually-similar Vehicles **(ICCV)** [pdf](http://openaccess.thecvf.com/content_ICCV_2017/papers/Yan_Exploiting_Multi-Grain_Ranking_ICCV_2017_paper.pdf)

4. Improving triplet-wise training of convolutional neural network for vehicle re-identification **(ICME)** [paper](https://ieeexplore.ieee.org/abstract/document/8019491)

5. Deep hashing with multi-task learning for large-scale instance-level vehicle search **(ICME workshop)** [paper](https://ieeexplore.ieee.org/abstract/document/8026274)

6. Multi-modal metric learning for vehicle re-identification in traffic surveillance environment **(ICIP)** [paper](https://ieeexplore.ieee.org/abstract/document/8296683)

7. Vehicle re-identification by fusing multiple deep neural networks **(IPTA)** [paper](https://ieeexplore.ieee.org/abstract/document/8310090)
 
8. Beyond human-level license plate super-resolution with progressive vehicle search and domain priori GAN **(ACMMM)** [paper](https://dl.acm.org/citation.cfm?id=3123422)
### **2016**
1. Vehicle Re-Identification for Automatic Video Traffic Surveillance **(CVPR workshop)** [pdf](http://openaccess.thecvf.com/content_cvpr_2016_workshops/w25/papers/Zapletal_Vehicle_Re-Identification_for_CVPR_2016_paper.pdf)

2. Deep Relative Distance Learning- Tell the Difference Between Similar Vehicles **(CVPR)** [pdf](http://openaccess.thecvf.com/content_cvpr_2016/papers/Liu_Deep_Relative_Distance_CVPR_2016_paper.pdf)

3. A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance **(ECCV)** [paper](https://link.springer.com/chapter/10.1007/978-3-319-46475-6_53)

4. Large-Scale Vehicle Re-Identification in Urban Surveillance Videos **(ICME)** [pdf](https://www.researchgate.net/profile/Xinchen_Liu/publication/303760492_Large-scale_vehicle_re-identification_in_urban_surveillance_videos/links/59e424090f7e9b97fbeb0ded/Large-scale-vehicle-re-identification-in-urban-surveillance-videos.pdf)

### Reference
- https://github.com/bismex/Awesome-vehicle-re-identification
- https://github.com/knwng/awesome-vehicle-re-identification