Duke is a subset of the DukeMTMC for image-based re-ID, in the format of the Market-1501 dataset. The original dataset contains 85-minute high-resolution videos from 8 different cameras. Hand-drawn pedestrain bounding boxes are available.
We crop pedestrain images from the videos every 120 frames, yielding in total 36,411 bounding boxes with IDs. There are 1,404 identities appearing in more than two cameras and 408 identities (distractor ID) who appear in only one camera. We randomly select 702 IDs as training set and the remaining 702 IDs as the testing set. In the testing set, we pick one query image for each ID in each camera and put the remaining images in the gallery.
As a result, we get 16,522 training images of 702 identities, 2,228 query images of the other 702 identities and 17,661 gallery images.
| File | Description |
|---|---|
| /bounding_box_test | The gallery images. We retrieve a query from this image pool. |
| /bounding_box_train | The training images. This dir contains the images from 702 different identities. |
| /query | The query images. Each of them is from different identities in different cameras. |
Naming Rule of the images In bbox "0005_c2_f0046985.jpg", "0005" is the identity. "c2" means the image from Camera 2. "f0046985" is the 46985th frame in the video of Camera 2.
This dataset is ONLY released for academic use. Please do not further distribute the dataset (including the download link), or put any of the images on the public website, due to the university regulations and privacy policy.
You can download the Duke dataset from GoogleDriver or (BaiduYun password:48z4).
If these links are unavailable, please don't hesitate to contact me to update links. Thank you.
To evaluate, you need to calculate your gallery and query feature (i.e., 17661x2048 and 2228x2048 matrix) and save them in advance. Then download the codes in this repository. You just need to change the image path and the feature path in the evaluation_res_duke_fast.m and run it to evaluate.
| Methods | Rank@1 | mAP | Reference |
|---|---|---|---|
| BoW+kissme | 25.13% | 12.17% | "Scalable person re-identification: a benchmark", Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, Qi Tian, ICCV 2015 |
| LOMO+XQDA | 30.75% | 17.04% | "Person Re-identification by Local Maximal Occurrence Representation and Metric Learning", Shengcai Liao, Yang Hu, Xiangyu Zhu, and Stan Z. Li, CVPR 2015 |
| Basel. | 65.22% | 44.99% | "Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro", Zheng, Zhedong and Zheng, Liang and Yang, Yi, arXiv:1701.07717 |
| Basel. + LSRO | 67.68% | 47.13% | "Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro", Zheng, Zhedong and Zheng, Liang and Yang, Yi, arXiv:1701.07717 |
Duke Dataset, Baseline Bibtex
Duke MTMC Dataset Bibtex
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