iLIDS stands for International Logistic Identification, and is an image dataset for re-identification of occluded pedestrians. The dataset contains 476 images of 119 pedestrians, some of which are occluded by other people or luggage. These images are taken by 4 non-overlapping cameras. The test set contains 238 images, and the validation set contains 238 images. This dataset can be used to train the learning model FPR (Foreground-aware Pyramid Reconstruction), thereby solving the problem of re-identification of occluded pedestrians.

Citation

@inproceedings{he2018deep, title={Deep spatial feature reconstruction for partial person re-identification: Alignment-free approach}, author={He, Lingxiao and Liang, Jian and Li, Haiqing and Sun, Zhenan} booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2018} } @inproceedings{he2019foreground, title={Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification}, author={He, Lingxiao and Wang, Yinggang and Liu, Wu and Zhao, He and Sun, Zhenan and Feng, Jiashi} booktitle={IEEE International Conference on Computer Vision (ICCV)}, year={2019} }