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 2016-11-22.

News

  • Two DukeMTMC re-id extension datasets added.
  • We are conducting a comprehensive evaluation and benchmark for person re-identification. Please check the preliminary version here.
  • Dataset Release time # identities # cameras Label method Crop size Multi-shot Tracking sequences Full frames availability
    VIPeR 2007 632 2 Hand 128X48
    ETH1,2,3 2007 85,35,28 1 Hand Vary
    QMUL iLIDS 2009 119 2 Hand Vary
    GRID 2009 1025 8 Hand Vary
    CAVIAR4ReID 2011 72 2 Hand Vary
    3DPeS 2011 192 8 Hand Vary ✔*
    PRID2011 2011 934 2 Hand 128X64 ✔*
    V47 2011 47 2 Hand Vary
    WARD 2012 70 3 Hand 128X48
    SAIVT-Softbio 2012 152 8 Hand Vary
    CUHK01 2012 971 2 Hand 160X60
    CUHK02 2013 1816 10(5 pairs) Hand 160X60
    CUHK03 2014 1467 10(5 pairs) Hand/DPM Vary
    RAiD 2014 43 4 Hand 128X64
    iLIDS-VID 2014 300 2 Hand Vary
    MPR Drone 2014 84 1 Pyramid Features(ACF) Vary
    HDA Person Dataset 2014 53 13 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 Hand/DPM 128X64
    PRW 2016 932 6 Hand vary
    Large scale person search 2016 8432 - Hand vary
    MARS 2016 1261 6 DPM+GMMCP 256X128
    DukeMTMC-reID 2017 1812 8 Hand Vary
    DukeMTMC4ReID 2017 1852 8 Doppia Vary
    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.

    viper re-identification
    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.

    eth
    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

    ilids
    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.

    GRID
    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).

    caviar
    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.

    3dpes
    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.

    PRID01

    PRID02

    PRID03
    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.

    V47
    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.

    WARD
    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.

    SAIVT
    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.

    cuhk01
    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.

    cuhk02
    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).

    cuhk03d

    cuhk03l
    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.

    RAiD
    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.

    ilidsvid1

    ilidsvid2

    ilidsvid3
    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.

    market1501
    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.

    market1501
    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).

    shi
    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.

    CASIA CASIAc CASIAb
    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).

    market
    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.

    market
    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.

    market
    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.

    mars
    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).

    dukereid

    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)

    duke4reid