Name |
What for |
Images |
Vehicles |
Models |
Cameras |
BoxCars21k |
Fine-Grained, Classification, Reidentification |
63,750 |
21,250 |
|
|
BoxCars116k |
Fine-Grained, Classification, Reidentification |
116,826 |
27,496 |
|
|
Car Highway |
Detection |
11,290 |
57,290 |
|
23 |
CompCars |
Classification |
214,345 |
|
1,716 |
|
DLR Vehicle Aerial |
Detection |
|
|
|
|
KITTI |
Detection, tracking, Optical Flow |
|
|
|
|
PKU-VD1 |
Classification (high-res) |
846,358 |
141,756 |
1,232 |
|
PKU-VD2 |
Classification (surveillance) |
690,518 |
79,763 |
1,112 |
|
PKU-VehicleID |
Reidentification |
221,763 |
26,267 |
|
|
Toy Car ReID |
Reidentification |
|
|
|
|
UA-DETRAC |
Detection |
>140,000 |
8,250 |
|
24 |
UTS dataset |
|
|
|
|
|
VEDAI |
|
|
|
|
|
VehicleID |
model Verification, reidentification |
221,763 |
26,267 |
|
|
Vehicle-1M |
Classification |
936,051 |
55,527 |
400 |
|
VeRi-776 |
Reidentification |
50,000 |
776 |
|
20 |
VeRi-Wild |
Reidentification |
416,314 |
40,671 |
|
174 |
Virtual KITTI (Unity) |
Detection, tracking |
50 videos (21,260) |
|
|
|
Virtual KITTI 2 (Unity) |
Detection, tracking |
|
|
|
|
VRIC |
Reidentification |
60,430 |
5,622 |
|
60 |
@article{Sochor2018,
author={J. Sochor and J. Špaňhel and A. Herout},
journal={IEEE Transactions on Intelligent Transportation Systems},
title={BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance},
year={2018},
volume={PP},
number={99},
pages={1-12},
doi={10.1109/TITS.2018.2799228},
ISSN={1524-9050}
}
Song, H., Liang, H., Li, H. et al. Vision-based vehicle detection and counting system using deep learning in highway scenes. Eur. Transp. Res. Rev. 11, 51 (2019). https://doi.org/10.1186/s12544-019-0390-4
Linjie Yang, Ping Luo, Chen Change Loy, Xiaoou Tang. A Large-Scale Car Dataset for Fine-Grained Categorization and Verification, In Computer Vision and Pattern Recognition (CVPR), 2015.
@inproceedings{yan2017exploiting,
title={Exploiting Multi-Grain Ranking Constraints for Precisely Searching Visually-Similar Vehicles},
author={Yan, Ke and Tian, Yonghong and Wang, Yaowei and Zeng, Wei and Huang, Tiejun},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={562--570},
year={2017}
}
@inproceedings{liu2016deep,
title={Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles},
author={Liu, Hongye and Tian, Yonghong and Wang, Yaowei and Pang, Lu and Huang, Tiejun},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={2167--2175},
year={2016}
}
For the stereo 2012, flow 2012, odometry, object detection or tracking benchmarks, please cite:
@INPROCEEDINGS{Geiger2012CVPR,
author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2012}
}
For the raw dataset, please cite:
@ARTICLE{Geiger2013IJRR,
author = {Andreas Geiger and Philip Lenz and Christoph Stiller and Raquel Urtasun},
title = {Vision meets Robotics: The KITTI Dataset},
journal = {International Journal of Robotics Research (IJRR)},
year = {2013}
}
For the road benchmark, please cite:
@INPROCEEDINGS{Fritsch2013ITSC,
author = {Jannik Fritsch and Tobias Kuehnl and Andreas Geiger},
title = {A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms},
booktitle = {International Conference on Intelligent Transportation Systems (ITSC)},
year = {2013}
}
For the stereo 2015, flow 2015 and scene flow 2015 benchmarks, please cite:
@INPROCEEDINGS{Menze2015CVPR,
author = {Moritz Menze and Andreas Geiger},
title = {Object Scene Flow for Autonomous Vehicles},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2015}
}
@article{CVIU_UA-DETRAC,
author = {Longyin Wen and Dawei Du and Zhaowei Cai and Zhen Lei and Ming{-}Ching Chang and
Honggang Qi and Jongwoo Lim and Ming{-}Hsuan Yang and Siwei Lyu},
title = { {UA-DETRAC:} {A} New Benchmark and Protocol for Multi-Object Detection and Tracking},
journal = {Computer Vision and Image Understanding},
year = {2020}
}
@inproceedings{lyu2018ua,
title={UA-DETRAC 2018: Report of AVSS2018 \& IWT4S challenge on advanced traffic monitoring},
author={Lyu, Siwei and Chang, Ming-Ching and Du, Dawei and Li, Wenbo and Wei, Yi and Del Coco, Marco and Carcagn{\`\i}, Pierluigi and Schumann, Arne and Munjal, Bharti and Choi, Doo-Hyun and others},
booktitle={2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
pages={1--6},
year={2018},
organization={IEEE}
}
@inproceedings{lyu2017ua,
title={UA-DETRAC 2017: Report of AVSS2017 \& IWT4S Challenge on Advanced Traffic Monitoring},
author={Lyu, Siwei and Chang, Ming-Ching and Du, Dawei and Wen, Longyin and Qi, Honggang and Li, Yuezun and Wei, Yi and Ke, Lipeng and Hu, Tao and Del Coco, Marco and others},
booktitle={Advanced Video and Signal Based Surveillance (AVSS), 2017 14th IEEE International Conference on},
pages={1--7},
year={2017},
organization={IEEE}
}
Zhou, Y., Liu, L., Shao, L. and Mellor, M., 2016, October. DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
Vehicle Detection in Aerial Imagery: A small target detection benchmark., Sébastien Razakarivony and Frédéric Jurie, Journal of Visual Communication and Image Representation, 2015
@inproceedings{liu2016deep,
title={Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles},
author={Liu, Hongye and Tian, Yonghong and Wang, Yaowei and Pang, Lu and Huang, Tiejun},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={2167--2175},
year={2016}
}
Haiyun Guo, Chaoyang Zhao, Zhiwei Liu, Jinqiao Wang, Hanqing Lu: Learning coarse-to-fine structured feature embedding for vehicle re-identification. AAAI 2018.
Xinchen Liu, Wu Liu, Huadong Ma, Huiyuan Fu: Large-scale vehicle re-identification in urban surveillance videos. ICME 2016: 1-6 (Best Student Paper Award, Citation=75)
Xinchen Liu, Wu Liu, Tao Mei, Huadong Ma: A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance. ECCV (2) 2016: 869-884 (Citation=56)
Xinchen Liu, Wu Liu, Tao Mei, Huadong Ma: PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance. IEEE Trans. Multimedia 20(3): 645-658 (2018) (Citation=26)
@inproceedings{lou2019large,
title={A Large-Scale Dataset for Vehicle Re-Identification in the Wild},
author={Lou, Yihang and Bai, Yan and Liu, Jun and Wang, Shiqi and Duan, Ling-Yu},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2019}
}
@inproceedings{Gaidon:Virtual:CVPR2016,
author = {Gaidon, A and Wang, Q and Cabon, Y and Vig, E},
title = {Virtual Worlds as Proxy for Multi-Object Tracking Analysis},
booktitle = {CVPR},
year = {2016}
}
@misc{cabon2020vkitti2,
title={Virtual KITTI 2},
author={Cabon, Yohann and Murray, Naila and Humenberger, Martin},
year={2020},
eprint={2001.10773},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
<a href="https://europe.naverlabs.com/wp-content/uploads/2020/01/vkitti2.pdf">pdf</a>
<a href="https://arxiv.org/pdf/2001.10773.pdf">arXiv</a>
@inproceedings{gaidon2016virtual,
title={Virtual worlds as proxy for multi-object tracking analysis},
author={Gaidon, Adrien and Wang, Qiao and Cabon, Yohann and Vig, Eleonora},
booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
pages={4340--4349},
year={2016}
}
<a href="https://europe.naverlabs.com/wp-content/uploads/ultimatemember/temp/2015-085.pdf">pdf</a>
@inproceedings{2018gcpr-Kanaci,
author = {Aytac Kanaci and Xiatian Zhu and Shaogang Gong},
title = {Vehicle Re-Identification in Context},
booktitle = {Pattern Recognition - 40th German Conference, {GCPR} 2018, Stuttgart,
Germany, September 10-12, 2018, Proceedings},
year = {2018}
}