{"id":13444387,"url":"https://github.com/layumi/Vehicle_reID-Collection","last_synced_at":"2025-03-20T18:32:27.630Z","repository":{"id":49851187,"uuid":"159405204","full_name":"layumi/Vehicle_reID-Collection","owner":"layumi","description":":red_car: the collection of vehicle re-ID papers, datasets. :red_car:","archived":false,"fork":false,"pushed_at":"2022-09-28T15:49:55.000Z","size":135,"stargazers_count":441,"open_issues_count":0,"forks_count":49,"subscribers_count":17,"default_branch":"master","last_synced_at":"2024-05-22T04:00:34.710Z","etag":null,"topics":["awesome","awesome-list","cvpr-workshop","dataset","deep-learning","paper","pku-vehicle","ve-ri","vehicle","vehicle-reid","veri776"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/layumi.png","metadata":{"files":{"readme":"Readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-11-27T22:00:19.000Z","updated_at":"2024-05-22T02:30:51.000Z","dependencies_parsed_at":"2022-09-10T03:21:22.478Z","dependency_job_id":null,"html_url":"https://github.com/layumi/Vehicle_reID-Collection","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/layumi%2FVehicle_reID-Collection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/layumi%2FVehicle_reID-Collection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/layumi%2FVehicle_reID-Collection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/layumi%2FVehicle_reID-Collection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/layumi","download_url":"https://codeload.github.com/layumi/Vehicle_reID-Collection/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":221550763,"owners_count":16841436,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["awesome","awesome-list","cvpr-workshop","dataset","deep-learning","paper","pku-vehicle","ve-ri","vehicle","vehicle-reid","veri776"],"created_at":"2024-07-31T04:00:21.566Z","updated_at":"2025-03-20T18:32:27.623Z","avatar_url":"https://github.com/layumi.png","language":null,"funding_links":[],"categories":["Uncategorized","Other Lists"],"sub_categories":["Uncategorized","TeX Lists"],"readme":"# Vehicle Re-ID Collection\n\nIf 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! \nor create pull request.\n\nPriorities are given to papers whose codes are published.\n\n## Code \n🏎️. 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)\n\n🚙: The 2nd Place Submission to AICity Challenge 2021 re-id track (CVPR 2021 workshop) [[code]](https://github.com/Xuanmeng-Zhang/AICITY2021-Track2)\n\n:red_car:  The 1st Place Submission to AICity Challenge 2020 re-id track (CVPR 2020 workshop) [[code]](https://github.com/layumi/AICIty-reID-2020)\n [[paper]](https://github.com/layumi/AICIty-reID-2020/blob/master/paper.pdf)\n \n :helicopter:  Drone-based building re-id (ACM Multimedia 2020) [[code]](https://github.com/layumi/University1652-Baseline)  [[paper]](https://arxiv.org/abs/2002.12186)\n \n 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)\n\n## Dataset\n1. VeRi-776\n\n    [project](https://github.com/VehicleReId/VeRidataset) [paper](https://link.springer.com/chapter/10.1007/978-3-319-46475-6_53)\n\n49,357 images of 776 vehicles from 20 cameras. Like Market-1501 protocol.\n\nThe 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.\n\n2. PKU Vehicle-ID\n\n    [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)\n\n221,763 images of 2,627 vehicles. Only two camera views??\n\n3. PKU-VD\n\n    [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)\n\nwith attribute.\n\n4. VehicleReID\n\n    [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)\n\n47,123 images from two cameras \u0026 lablled on pair.\n\n5. PKU-Vehicle\n\n    [project](http://59.110.216.11/html/) [paper](https://ieeexplore.ieee.org/abstract/document/8265213)\n\nno ID lablled.\n\n6. CompCars\n\n    [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) \n\n136,726 + 27,618 images of 1,716 cars with attributes. After crop,  136,713.\n\n7. StanfordCars\n\n    [project](http://ai.stanford.edu/~jkrause/cars/car_dataset.html) [pdf](http://ai.stanford.edu/~jkrause/papers/3drr13.pdf)\n\n16,185 images of 196 classes.\n\n8. Vehicle-1M\n\n    [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)\n    \n    \n9. VERI-Wild \n\n[project](https://github.com/PKU-IMRE/VERI-Wild)\n\n\n10. VRIC \nwith various motion blur and resolution\n\n[project](https://qmul-vric.github.io) \n\n 60,430 images of 5,622 vehicle identities captured by 60 different cameras \n\n## Recent Papers\n\n### **2025**\n1. Coarse-to-Fine Cross-modality Generation for Enhancing Vehicle Re-Identification with High-Fidelity Synthetic Data **(ICRA)** [paper](https://www.zdzheng.xyz/files/ICRA25-Vehicle.pdf)\n\n### **2021**\n1. TransReID: Transformer-based Object Re-Identification **(ICCV)** \n[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/He_TransReID_Transformer-Based_Object_Re-Identification_ICCV_2021_paper.pdf) \n[code](https://github.com/damo-cv/TransReID)\n\n2. Viewpoint and Scale Consistency Reinforcement for UAV Vehicle Re-Identification **(IJCV)** \n[pdf](https://link.springer.com/content/pdf/10.1007/s11263-020-01402-2.pdf)\n\n3. Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-Identification **(TITS)** \n[paper](https://ieeexplore.ieee.org/abstract/document/9457192)\n\n4. PhD Learning: Learning With Pompeiu-Hausdorff Distances for Video-Based Vehicle Re-Identification **(CVPR)** \n[paper](http://openaccess.thecvf.com//content/CVPR2021/html/Zhao_PhD_Learning_Learning_With_Pompeiu-Hausdorff_Distances_for_Video-Based_Vehicle_Re-Identification_CVPR_2021_paper.html) \n[code](https://github.com/emdata-ailab/PhD-Learning)\n\n5. Heterogeneous Relational Complement for Vehicle Re-identification **(ICCV)** \n[paper](https://openaccess.thecvf.com/content/ICCV2021/html/Zhao_Heterogeneous_Relational_Complement_for_Vehicle_Re-Identification_ICCV_2021_paper.html) \n[code](https://github.com/iCVTEAM/HRCN)\n\n6. Model Latent Views With Multi-Center Metric Learning for Vehicle Re-Identification **(TITS)** \n[paper](https://ieeexplore.ieee.org/document/9325909/)\n\n7. Inter-Domain Adaptation Label for Data Augmentation in Vehicle Re-identification **(TMM)** \n[paper](https://ieeexplore.ieee.org/abstract/document/9513554)\n\n8. Learning Multiple Semantic Knowledge For Cross-Domain Unsupervised Vehicle Re-Identification **(ICME)** \n[paper](https://ieeexplore.ieee.org/abstract/document/9428440)\n\n9. Multi-level Deep Learning Vehicle Re-identification using Ranked-based Loss Functions **(ICPR)** \n[paper](https://ieeexplore.ieee.org/abstract/document/9412415)\n\n10. Keypoint-Aligned Embeddings for Image Retrieval and Re-Identification **(WACV)** \n[pdf](https://openaccess.thecvf.com/content/WACV2021/papers/Moskvyak_Keypoint-Aligned_Embeddings_for_Image_Retrieval_and_Re-Identification_WACV_2021_paper.pdf)\n\n11. Pseudo Graph Convolutional Network for Vehicle ReID **(ACMMM)** \n[paper](https://dl.acm.org/doi/abs/10.1145/3474085.3475462)\n\n12. Vehicle Re-identification for Lane-level Travel Time Estimations on Congested Urban Road Networks Using Video Images **(TITS)** \n[paper](https://ieeexplore.ieee.org/abstract/document/9569748)\n\n13. OERFF: A Vehicle Re-Identification Method Based on Orientation Estimation and Regional Feature Fusion **(IEEE Access)** \n[paper](https://ieeexplore.ieee.org/abstract/document/9416706)\n\n14. Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification **(ICCV)** \n[arXiv](https://arxiv.org/abs/2108.08728) \n[code](https://github.com/raoyongming/CAL)\n\n15. Self-Supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond **(ICCV)** \n[pdf](https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Self-Supervised_Geometric_Features_Discovery_via_Interpretable_Attention_for_Vehicle_Re-Identification_ICCV_2021_paper.pdf)\n\n### **2020**\n1. VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification **(TMM)**\n[arXiv](https://arxiv.org/abs/2004.06305) \n[[中文介绍]](https://zhuanlan.zhihu.com/p/186905783)\n\n2. Beyond the Parts: Learning Multi-view Cross-part Correlation for Vehicle Re-identification **(ACM MM)** \n[paper](http://xinchenliu.com/papers/2020_ACMMM_PCRNet.pdf) \n[code](https://github.com/lxc86739795/parsing_platform)\n\n3. The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification **(ECCV)** \n[arXiv](https://arxiv.org/abs/2004.06271) \n[[中文介绍]](https://zhuanlan.zhihu.com/p/191654655)\n\n4. Structural Analysis of Attributes for Vehicle Re-Identification and Retrieval **(TITS)** \n[paper](https://ieeexplore.ieee.org/document/8643580)\n\n5. Group-Group Loss-Based Global-Regional Feature Learning for Vehicle Re-Identification **(TIP)** \n[paper](https://ieeexplore.ieee.org/document/8897720) \n[code](https://github.com/liu-xb/GGL)\n\n6. Simulating Content Consistent Vehicle Datasets with Attribute Descent **(ECCV)** \n[pdf](https://link.springer.com/content/pdf/10.1007/978-3-030-58539-6_46.pdf) \n[code](https://github.com/yorkeyao/VehicleX)\n[[中文介绍]](https://zhuanlan.zhihu.com/p/198061566)\n\n7. Parsing-based View-aware Embedding Network for Vehicle Re-Identification **(CVPR)** \n[paper](https://openaccess.thecvf.com/content_CVPR_2020/html/Meng_Parsing-Based_View-Aware_Embedding_Network_for_Vehicle_Re-Identification_CVPR_2020_paper.html)\n[code](https://github.com/silverbulletmdc/PVEN)\n[[中文介绍]](https://zhuanlan.zhihu.com/p/160877803)\n\n8. Robust Re-Identification by Multiple Views Knowledge Distillation **(ECCV)** [paper](https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/996_ECCV_2020_paper.php) \n[code](https://github.com/aimagelab/VKD)\n\n9. Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network **(ECCV)** \n[pdf](https://paperswithcode.com/paper/orientation-aware-vehicle-re-identification) \n[code](https://github.com/tsaishien-chen/SPAN)\n\n10. Vehicle Re-Identification Using Quadruple Directional Deep Learning Features **(TITS)** \n[paper](https://ieeexplore.ieee.org/document/8667847)\n\n11. Multi-Spectral Vehicle Re-Identification: A Challenge **(AAAI)** \n[paper](https://ojs.aaai.org//index.php/AAAI/article/view/6796)\n\n12. Unsupervised Vehicle Re-identification with Progressive Adaptation **(IJCAI)** \n[paper](https://www.ijcai.org/proceedings/2020/127)\n\n13. Disentangled Feature Learning Network for Vehicle Re-Identification **(IJCAI)** \n[paper](https://www.ijcai.org/proceedings/2020/66)\n\n14. CFVMNet: A Multi-branch Network for Vehicle Re-identification Based on Common Field of View **(ACMMM)** \n[paper](https://dl.acm.org/doi/10.1145/3394171.3413541)\n\n15. A Structured Graph Attention Network for Vehicle Re-Identification **(ACMMM)** \n[paper](https://dl.acm.org/doi/10.1145/3394171.3413607)\n\n16. Fine-grained Feature Alignment with Part Perspective Transformation for Vehicle ReID **(ACMMM)** \n[paper](https://dl.acm.org/doi/abs/10.1145/3394171.3413573)\n\n17. Background Segmentation for Vehicle Re-identification **(MMM)** \n[paper](https://link.springer.com/chapter/10.1007/978-3-030-37734-2_8)\n\n18. Dual Domain Multi-Task Model for Vehicle Re-Identification **(TITS)** \n[paper](https://ieeexplore.ieee.org/abstract/document/9226133/)\n\n19. Multi-View Spatial Attention Embedding for Vehicle Re-Identification **(TCSVT)** \n[paper](https://ieeexplore.ieee.org/abstract/document/9033992)\n\n20. Unsupervised domain adaptive re-identification: Theory and practice **(PR)** \n[paper](https://www.sciencedirect.com/science/article/pii/S003132031930473X)\n\n21. VARID: Viewpoint-Aware Re-IDentification of Vehicle Based on Triplet Loss **(TITS)** \n[paper](https://ieeexplore.ieee.org/abstract/document/9210535)\n\n22. Uncertainty-Aware Multi-Shot Knowledge Distillation for Image-Based Object Re-Identification **(AAAI)** \n[paper](https://ojs.aaai.org/index.php/AAAI/article/view/6774)\n\n23. Tell The Truth From The Front: Anti-Disguise Vehicle Re-Identification **(ICME)** \n[paper](https://ieeexplore.ieee.org/abstract/document/9102939)\n\n24. Vehicle Re-Identification Using Distance-Based Global and Partial Multi-Regional Feature Learning **(TITS)** \n[paper](https://ieeexplore.ieee.org/abstract/document/8972901)\n\n### **2019**\n1. VR-PROUD: Vehicle Re-identification using PROgressive Unsupervised Deep architecture **(PR)** [paper](https://www.sciencedirect.com/science/article/abs/pii/S0031320319300147)\n\n2. Embedding Adversarial Learning for Vehicle Re-Identification **(TIP)** [paper](https://ieeexplore.ieee.org/abstract/document/8653852)\n\n3. Vehicle Re-Identification Using Quadruple Directional Deep Learning Features **(TITS)** [pdf](https://arxiv.org/pdf/1811.05163.pdf)\n\n4. 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)\n\n5. Part-regularized Near-duplicate Vehicle Re-identification **(CVPR)** [pdf](http://cvteam.net/papers/2019_CVPR_Part-regularized%20Near-duplicate%20Vehicle%20Re-identification.pdf)\n\n### **2018**\n1. 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)\n\n2. 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)\n\n3. 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)\n\n4. Fast vehicle identification via ranked semantic sampling based embedding **(IJCAI)** [pdf](https://www.ijcai.org/proceedings/2018/0514.pdf)\n\n5. Vehicle re-identification by deep hidden multi-view inference **(TIP)** [paper](https://ieeexplore.ieee.org/abstract/document/8325486)\n\n6. Ram: a region-aware deep model for vehicle re-identification **(ICME)** [pdf](https://arxiv.org/pdf/1806.09283.pdf)\n\n7. 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)\n\n8. PROVID- Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance **(TMM)** [paper](https://ieeexplore.ieee.org/abstract/document/8036238)\n\n9. Group Sensitive Triplet Embedding for Vehicle Re-identification **(TMM)** [paper](https://ieeexplore.ieee.org/abstract/document/8265213)\n\n10. VP-ReID: vehicle and person re-identification system **(ACMMM)** [paper](https://dl.acm.org/citation.cfm?id=3206086)\n\n11. Vehicle Re-Identification by Adversarial Bi-Directional LSTM Network **(WACV)** [paper](https://ieeexplore.ieee.org/abstract/document/8354181/)\n\n12. Joint Semi-supervised Learning and Re-ranking for Vehicle Re-identification **(ICPR)** [paper](https://ieeexplore.ieee.org/abstract/document/8545584/)\n\n13. Multi-Attribute Driven Vehicle Re-Identification with Spatial-Temporal Re-Ranking **(ICIP)** [paper](https://ieeexplore.ieee.org/abstract/document/8451776/)\n\n14. Joint feature and similarity deep learning for vehicle re-identification **(IEEE Access)** [paper](https://ieeexplore.ieee.org/abstract/document/8424333/)\n### **2017**\n1. 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)\n\n2. 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)\n\n3. 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)\n\n4. Improving triplet-wise training of convolutional neural network for vehicle re-identification **(ICME)** [paper](https://ieeexplore.ieee.org/abstract/document/8019491)\n\n5. Deep hashing with multi-task learning for large-scale instance-level vehicle search **(ICME workshop)** [paper](https://ieeexplore.ieee.org/abstract/document/8026274)\n\n6. Multi-modal metric learning for vehicle re-identification in traffic surveillance environment **(ICIP)** [paper](https://ieeexplore.ieee.org/abstract/document/8296683)\n\n7. Vehicle re-identification by fusing multiple deep neural networks **(IPTA)** [paper](https://ieeexplore.ieee.org/abstract/document/8310090)\n \n8. 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)\n### **2016**\n1. 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)\n\n2. 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)\n\n3. 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)\n\n4. 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)\n\n### Reference\n- https://github.com/bismex/Awesome-vehicle-re-identification\n- https://github.com/knwng/awesome-vehicle-re-identification\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flayumi%2FVehicle_reID-Collection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flayumi%2FVehicle_reID-Collection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flayumi%2FVehicle_reID-Collection/lists"}