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 ||Market ||CUHK03 ||CUHK01 ||VIPeR ||PRID ||CAVIAR |
If you use this dataset in your research, please kindly cite our work as,
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
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