In this project page, we mainly introduce the MSMT17 dataset.



Description to MSMT17




To collect a large-scale person re-identification dataset-MSMT17, we utilize an 15-camera network deployed in campus. This camera network contains 12 outdoor cameras and 3 indoor cameras. We select 4 days with different weather conditions in a month for video collection. For each day, 3 hours of videos taken in the morning, noon, and afternoon, respectively, are selected for pedestrian detection and annotation. Our final raw video set contains 180 hours of videos, 12 outdoor cameras, 3 indoor cameras, and 12 time slots. Faster RCNN is utilized for pedestrian bounding box detection. Three labelers go through the detected bounding boxes and annotate ID label for 2 months. Finally, 126,441 bounding boxes of 4,101 identities are annotated. Some statistics on MSMT17 are shown in above. Compared with existing datasets, we summarize the new features in MSMT17 into the following aspects:

(1) Larger number of identities, bounding boxes, and cameras.
(2) Complex scenes and backgrounds.
(3) Multiple time slots result in severe lighting changes.
(4) More reliable bounding box detector.

Dataset MSMT17 Duke [1] Market [2] CUHK03 [3] CUHK01 [4] VIPeR [5] PRID [6] CAVIAR [7]
BBoxes 126,441 36,411 32,668 28,192 3,884 1,264 1,134 610
Identities 4,101 1,812 1,501 1,467 971 632 934 72
Cameras 15 8 6 2 10 2 2 2
Detector Faster RCNN hand DPM DPM,hand hand hand hand hand
Scene outdoor,indoor outdoor outdoor indoor indoor outdoor outdoor indoor


If you use this dataset in your research, please kindly cite our work as,

New!

The summary state of the art methods on MSMT17 is released.

The dataset on MSMT17 has been released.

The evaluation code on MSMT17 has been released.

0000_008_01_0303morining_0019_2.jpg represents the image was captured from camera1
0000_009_05_0303morining_0029_1.jpg represents the image was captured from camera5

Reference

[1] Z. Zheng et al. Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In ICCV, 2017.
[2] L. Zheng et al. Scalable person re-identification: A benchmark. In ICCV, 2015.
[3] W. Li et al. Deepreid: Deep filter 918 pairing neural network for person re-identification. In CVPR, 2014.
[4] W. Li et al. Human reidentification with transferred metric learning. In ACCV, 2012.
[5] D. Gray et al. Viewpoint invariant pedestrian recogni- tion with an ensemble of localized features. In ECCV, 2008.
[6] M. Hirzer et al. Person re-identification by descriptive and discriminative classifica- tion. In SCIA, 2011.
[7] D. S. Cheng et al. Custom pictorial structures for re-identification. In BMVC, 2011.