行人重识别数据集汇总

最近一段时间在做行人重识别方向的研究，行人重识别（Person Re-Identification）作为图像识别领域的一个分支，在实际生活中具有极其重要的意义。目前，城市里的用于公共治安领域的摄像头已经大量部署，几乎到了几十米到几百米一个覆盖的程度，尽管如此，不同的摄像头之间仍然存在无法覆盖的区域。行人再识别的目标就是要确定一个摄像头下发现的目标，在离开摄像头的视野后去哪了，这有点像视频搜索，就是在其他的摄像头采集到的视频中找到目标。目前行人检测的算法如DPM和Fast RCNN已经可以实现在一个图像中圈出行人的目标，即自动标注，不需要人工去标注了，那么行人重识别任务就是要找到最可能匹配待识别目标的候选。目前的行人再识别研究是基于数据集的，就是通过架设几个摄像头，采集行人图像，然后人工标注或自动标注。这些图像一部分用于训练，一部分用于识别。目前的识别率还达不到可以应用的要求，为了提高识别精度，识别算法主要分为两部分，一部分是提取更好的图像特征，另一部分是为了更有效的计算比对不同特征之间的距离。已有一些客观的评价标准，用于比较各种算法的好坏。研究者们也造出了一些数据集，从简单到庞大。后来者可以重用其贡献的成果。

下面是美国波士顿东北大学总结的行人重识别数据集，原文地址（http://robustsystems.coe.neu.edu/sites/robustsystems.coe.neu.edu/files/systems/projectpages/reiddataset.html）

 

Person Re-identification Datasets

 

Person re-identification has drawn intensive attention in the computer vision society in recent decades. As far as we know, this page collects all public datasets that have been tested by person re-identification algorithms. If you use any of them, please refer to the original licence. If you have any suggestions or you want to include your dataset here, please send the link of the dataset to m...@coe.neu.edu
The last update is 2018-04-16.

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News

• MSMT17 data added.
• The code base for our benchmark paper is released! [GitHub]
• The lastest version of the systematic evaluation and benchmark for person re-identification is released!
• Airport dataset added.

 

Dataset	Release time	# identities	# cameras	# images	Label method	Crop size	Multi-shot	Tracking sequences	Full frames availability
VIPeR	2007	632	2	1264	Hand	128X48
ETH1,2,3	2007	85,35,28	1	8580	Hand	Vary	✔	✔	✔
QMUL iLIDS	2009	119	2	476	Hand	Vary	✔
GRID	2009	1025	8	1275	Hand	Vary
CAVIAR4ReID	2011	72	2	1220	Hand	Vary	✔
3DPeS	2011	192	8	1011	Hand	Vary	✔		✔*
PRID2011	2011	934	2	24541	Hand	128X64	✔	✔	✔*
V47	2011	47	2	752	Hand	Vary	✔		✔
WARD	2012	70	3	4786	Hand	128X48	✔	✔
SAIVT-Softbio	2012	152	8	64472	Hand	Vary	✔	✔	✔
CUHK01	2012	971	2	3884	Hand	160X60	✔
CUHK02	2013	1816	10(5 pairs)	7264	Hand	160X60	✔
CUHK03	2014	1467	10(5 pairs)	13164	Hand/DPM	Vary	✔
RAiD	2014	43	4	6920	Hand	128X64	✔
iLIDS-VID	2014	300	2	42495	Hand	Vary	✔	✔
MPR Drone	2014	84	1		Pyramid Features(ACF)	Vary	✔		✔
HDA Person Dataset	2014	53	13	2976	Hand/Pyramid Features(ACF)	Vary	✔	✔	✔
Shinpuhkan Dataset	2014	24	16		Hand	128X48	✔	✔
CASIA Gait Database B	2015(*see below)	124	11		Background subtraction	Vary	✔	✔	✔
Market1501	2015	1501	6	32217	Hand/DPM	128X64	✔
PKU-Reid	2016	114	2	1824	Hand	128X64
PRW	2016	932	6	34304	Hand	vary	✔		✔
Large scale person search	2016	11934s	-	34574	Hand	vary			✔
MARS	2016	1261	6	1191003	DPM+GMMCP	256X128	✔	✔
DukeMTMC-reID	2017	1812	8	36441	Hand	Vary	✔		✔
DukeMTMC4ReID	2017	1852	8	46261	Doppia	Vary	✔		✔
Airport	2017	9651	6	39902	ACF	128X64	✔
MSMT17	2018	4101	15	126441	Faster RCNN	Vary	✔

VIPeR [link]

This dataset contains two cameras, each of which captures one image per person. It also provides the viewpoint angle of each image. Although it has been tested by many researchers, it's still one of the most challenging datasets. Ryan Layne provides the attribute annotation of VIPeR here.

Ref: D. Gray, and H. Tao, "Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features," in Proc. European Conference on Computer Vision (ECCV), 2008.

ETH [link]

Different from other datasets collecting images from multiple cameras, ETHZ collects images from a moving camera. Although the viewpoint variance is relatively small, it does have considerable illumination variance, scale variance and occlusion.

Ref: W.R. Schwartz, L.S. Davis. Learning Discriminative Appearance-Based Models Using Partial Least Squares. Proceedings of the XXII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'2009), Rio de Janeiro, Brazil, October 11-14, 2009.

QMUL iLIDS [link]

QMUL iLIDS is based on iLIDS MCTS, a dataset collected in an airport during busy time by a multi-camera CCTV system. Almost every identity has four images from two non-overlapping cameras. This dataset has senarios with heavy occlusion and pose variance.

Zheng et al. Associating Groups of People, BMVC 2009

GRID [link]

GRID is collected by 8 disjoint cameras in a busy underground station. Each identity has two images from different views and there are more images in the gallery set than the probe set. The image quality of this dataset is fairly poor.

Ref: Loy, C. C., Liu, C., & Gong, S. (2013, September). Person re-identification by manifold ranking. In 2013 IEEE International Conference on Image Processing (pp. 3567-3571). IEEE.

CAVIAR4ReID [link]

This dataset is extracted from a multi-target tracking dataset CAVIAR, which is collected in a shopping mall by two surveillance cameras with overlapped view field. Among 72 identities, 50 of them have images from two camera views and the rest 22 only from one camera. Images for each identity are carefully selected to maximize the resolustion variance.

Ref: Cheng, D. S., Cristani, M., Stoppa, M., Bazzani, L., & Murino, V. (2011, September). Custom Pictorial Structures for Re-identification. In BMVC (Vol. 1, No. 2, p. 6).

3DPeS [link]

3DPeS dataset is collected by 8 non-overlapped outdoor cameras. Although the original video is provided, researchers always use the selected snapshots to test person re-identification algorithms. It has 3D model for the environment and the calibration data for all cameras. In video sequences, only the bounding boxes of the first appearing frame of each identity are provided.

Ref: Baltieri, D., Vezzani, R., & Cucchiara, R. (2011, December). 3dpes: 3d people dataset for surveillance and forensics. In Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding (pp. 59-64). ACM.

PRID2011 [link]

PRID dataset has 385 trajectories from camera A and 749 trajectories from camera B. Among them, only 200 people appear in both cameras. This dataset also has a single shot version, which consists with random selected snapshots. Some trajectories are not well-synchronized, which means the person might "jump" between consecutive frames.

Ref: Hirzer, M., Beleznai, C., Roth, P. M., & Bischof, H. (2011, May). Person re-identification by descriptive and discriminative classification. In Scandinavian conference on Image analysis (pp. 91-102). Springer Berlin Heidelberg.

 

 

V47 [link] [Citation]

V47 dataset is collected using two indoor cameras with overlapped field of view. Each identity walks in two different directions (in and out) and is captured in several different viewpoints.

 

WARD [link]

This dataset is collected with three non-overlaping cameras. Each identity has several images in each camera. Although the images seem to be a labeled trajectory, it's not guaranteed by the author.

Ref: Martinel, N., & Micheloni, C. (2012, June). Re-identify people in wide area camera network. In 2012 IEEE computer society conference on computer vision and pattern recognition workshops (pp. 31-36). IEEE.

SAIVT-Softbio [link]

SAIVT-Softbio is collected by eight existing surveillance cameras. Since it's an uncontrolled collection, most identities only pass through a subset of cameras. This dataset also provides the whole video frames with labeled bounding box on every frame, but the bounding boxes are not very tight for some instances.

Ref: Bialkowski, Alina, Denman, Simon, Lucey, Patrick, Sridharan, Sridha, & Fookes, Clinton B. (2012) A database for person re-identification in multi-camera surveillance networks. In Proceedings of the 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA 12), IEEE, Esplanade Hotel, Fremantle, WA, pp. 1-8.

CUHK01 [link]

CUHK01 dataset contains two images for every identity from each camera. This dataset has one pair disjoint cameras and the image quality of this dataset is relatively good.

Ref: W. Li, R. Zhao and X. Wang, "Human Reidentification with Transferred Metric Learning" in Proceedings of Asian Conference on Computer Vision (ACCV) 2012.

CUHK02 [link]

CUHK02 is an extended dataset from CUHK01. Besides the camera pair in CUHK01, it has four more camera pair settings.

Ref: W. Li and X. Wang, "Locally Aligned Feature Transforms across Views" in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2013.

CUHK03 [link]

CUHK03 is the first person re-identification dataset that is large enough for deep learning. It provides the bounding boxes detected from deformable part models (DPM) and manually labeling. Person detection quality is relatively good for this dataset.

Ref: Li, W., Zhao, R., Xiao, T., & Wang, X. (2014). Deepreid: Deep filter pairing neural network for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 152-159).

 

RAiD [link]

As a relatively new released dataset, RAiD guaranteed each identity has images in all four non-overlaping cameras. Since two cameras are indoor and the other two are outdoor, the illumination variance is considerably large. Images of each identity are collected in a tracking manner, but the order is not always consistent.

Ref: Das, A., Chakraborty, A., & Roy-Chowdhury, A. K. (2014, September). Consistent re-identification in a camera network. In European Conference on Computer Vision (pp. 330-345). Springer International Publishing.

iLIDS-VID [link]

Based on the assumption that the real person re-identification system should have the trajectory for each identity, iLIDS-VID dataset extracted 600 trajectories for 300 identities from iLIDS MCTS dataset. Due to the limitation of iLIDS MCTS dataset, iLIDS-VID has extremely heavy occlusion.

Ref: Wang, T., Gong, S., Zhu, X., & Wang, S. (2016). Person Re-Identification by Discriminative Selection in Video Ranking.

 

 

MPR Drone [link]

MPR Drone dataset is not a traditional person person re-identification dataset with images captured across a camera network. Instead, it is collected by a flying drone in both indoor and outdoor environment. Since it only has one camera, the author proposed three different types of evaluation experiments in the original paper. All pedestrian detections are obtained by pyramid feature detection in Piotr Dollar's toolbox. It has two sub-datasets. Dataset 01 has been exhaustively labeled for 113610 detections. Dataset 02 provides the raw frame data for Dataset 01.

Ref: Layne, R., Hospedales, T. M., & Gong, S. (2014, September). Investigating Open-World Person Re-identification Using a Drone. In European Conference on Computer Vision (pp. 225-240). Springer International Publishing.

HDA+ [link]

HDA dataset is proposed to mimic the real person re-identification system as close as possible. Within it, 85 persons were densely labeled across 13 cameras during 30 mins. In addition to the tight bounding boxes, the author also proivdes the occlusion flag, camera homographies and synchronization. Image qualities are vary from 640x480 to 2560x1600 and FPSs are vary from 1 to 5. A nice evaluation tool is provided to test the re-id algorithm, person detector or both of them. Six different protocols are included to analysis the whole re-id system. Detections from ACF are provided.

Ref: Figueira, D., Taiana, M., Nambiar, A., Nascimento, J., & Bernardino, A. (2014, September). The hda+ data set for research on fully automated re-identification systems. In European Conference on Computer Vision (pp. 241-255). Springer International Publishing.

Shinpuhkan Dataset [link]

Shinpuhkan dataset was orginally created to test multi-camera tracking methods. Each person has multiple tracklets in different directions within each camera. In total, each identity has 86 annotated tracklets. The image quality seems fairly good comparing with other tranditional re-id datasets.

Ref: Kawanishi, Y., Wu, Y., Mukunoki, M., & Minoh, M. (2014). Shinpuhkan2014: A multi-camera pedestrian dataset for tracking people across multiple cameras. In 20th Korea-Japan Joint Workshop on Frontiers of Computer Vision (Vol. 5, p. 6).

CASIA Gait Database B [link]

CASIA dataset was created in 2005 and originally used to test gait recognition algorithm. In 2015, Liu et.al reused this dataset to test a gait-based person re-identification algorithm. This dataset is collected by 11 overlapped cameras in different view angles from 0 to 180 degree. Each identity also changes the clothing and carrying condition. Instead of providing bounding boxes, the raw video frames and the silhouette of each frame are given.

 

Market1501 [link]

It contains a large number of identities and each identity has several images from six dis-joint cameras. This dataset also includes 2793 false alarms from DPM as distractors to mimic the real scenario. Quality of the bounding boxes is worse than CUHK03. Later in the ICCV 2015 release version, 500K distractors are integrated to make this dataset really large scale. In the original paper proposed this dataset, the author also used mAP as an evalution criteria to test the algorithms.

Ref: Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015). Scalable person re-identification: A benchmark. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1116-1124).

PKU-Reid [link]

PKU-Reid dataset is relatively small comparing with other modern re-id datasets. The key feature of this dataset is that it captures person appearance from all eight orientations in two disjoint cameras.

Ref: Ma, L., Liu, H., Hu, L., Wang, C., & Sun, Q. (2016). Orientation Driven Bag of Appearances for Person Re-identification. arXiv preprint arXiv:1605.02464.

PRW [link]

The PRW (Person Re-identification in the Wild) dataset is an extenstion of Maretk1501 dataset. Instead of only provide bounding boxes, the author released the full frames with annotations. Therefore one can evaluate the affact of different person detectors.

Ref: Zheng, L., Zhang, H., Sun, S., Chandraker, M., & Tian, Q. (2016). Person Re-identification in the Wild. arXiv preprint arXiv:1604.02531.

Large scale person search [link]

Similar to PRW dataset, the person search dataset is large scale dataset with full frame access and large amount of labeled bounding boxes. It aims to mimic the real scenario of person search. Therefore, to test this dataset, a reliable person detector is needed. To make the dataset more difficult, the gallery part includes frames from hand held camera and movies. Two more subsets, low-resolution subset and occlusion subset, are also released to evalution the affect of those factors.

Ref: Xiao, T., Li, S., Wang, B., Lin, L., & Wang, X. (2016). End-to-End Deep Learning for Person Search. arXiv preprint arXiv:1604.01850.

MARS [link]

The MARS (Motion Analysis and Re-identification Set) dataset is an extenstion verion of the Market1501 dataset. It is the first large scale video based person re-id datset. Since all bounding boxes and tracklets are generated automatically, it contains distractors and each identity may have more than one tracklets. Precomputed deep features are also avaliable on the website.

Ref: Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., & Tian, Q. (2016, October). Mars: A video benchmark for large-scale person re-identification. In European Conference on Computer Vision (pp. 868-884). Springer International Publishing.

DukeMTMC-reID/DukeMTMC4ReID [link]

The DukeMTMC dataset is a large-scale heavily labeled multi-target multi-camera tracking dataset. In total, more than 2700 people were labeled with unique identities in 8 cameras. With the access to all information (full frames, frame level ground truth, calibration information, etc.), this dataset has a lot of protentials. Based on the released train-validation set, two re-id extension datasets are created. The key difference is the way to generate the bounding boxes. DukeMTMC-reID directly uses the manually labeled ground truth whereas DukeMTMC4ReID adopts Doppia as the person detector.

 

DukeMTMC-reID
Zheng, Zhedong, Liang Zheng, and Yi Yang. "Unlabeled samples generated by gan improve the person re-identification baseline in vitro." arXiv preprint arXiv:1701.07717 (2017).

 

DukeMTMC4ReID
Gou, Mengran and Karanam, Srikrishna and Liu, Wenqian and Camps, Octavia and Radke, Richard J. "DukeMTMC4ReID: A Large-Scale Multi-Camera Person Re-Identification Dataset." CVPR Workshops (2017)

Airport [link]

The dataset was created using videos from six cameras of an indoor surveillance network in a mid-sized airport. The cameras cover various parts of a central security checkpoint area and three concourses. Each camera has 768 × 432 pixels and captures video at 30 frames per second. 12-hour long videos from 8 AM to 8 PM were collected from each of these cameras. Under the assumption that each target person takes a limited amount of time to travel through the network, each of these long videos was randomly split into 40 five minute long video clips. Each video clip was then run through a prototype end-to- end re-id system comprised of automatic person detection and tracking algorithms.

Ref: Karanam, S., Gou, M., Wu, Z., Rates-Borras, A., Camps, O., & Radke, R. J. (2018). IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.

MSMT17 [link]

This large scale re-id dataset is collected in a campus with 12 outdoor cameras and 3 indoor cameras. It coveres 4 days with different weather in a month. For each day, 3 one-hour videos are selected from morning, noon and afternoon. Faster RCNN is utilized for pedestrian detection. This dataset is the largest re-id dataset so far. It has similar viewpoint with Market, but much more complicated scenarios.

Ref: Wei, L., Zhang, S., Gao, W., & Tian, Q. (2018). Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. Computer Vision and Pattern Recognition, IEEE International Conference on, 2018