Shuai Shao*, Zijian Zhao*, Boxun Li, Tete Xiao, Gang Yu, Xiangyu Zhang, Jian Sun
CrowdHuman is a benchmark dataset to better evaluate detectors in crowd scenarios. The CrowdHuman dataset is large, rich-annotated and contains high diversity. CrowdHuman contains 15000, 4370 and 5000 images for training, validation, and testing, respectively. There are a total of 470K human instances from train and validation subsets and 23 persons per image, with various kinds of occlusions in the dataset. Each human instance is annotated with a head bounding-box, human visible-region bounding-box and human full-body bounding-box. We hope our dataset will serve as a solid baseline and help promote future research in human detection tasks.
[news] Our dataset is published.
Volume, density and diversity of different human detection datasets.
For fair comparison, we only show the statistics of training subset.
Comparison of the visible ratio between our CrowdHuman and CityPersons dataset.
Visible Ratio is defined as the ratio of visible bounding box to the full bounding box.
Detection In the Wild Challenge Workshop 2019
The competitions platform is provided by Biendata.
Please cite the following paper if you use our dataset.
@article{shao2018crowdhuman,
title={CrowdHuman: A Benchmark for Detecting Human in a Crowd},
author={Shao, Shuai and Zhao, Zijian and Li, Boxun and Xiao, Tete and Yu, Gang and Zhang, Xiangyu and Sun, Jian},
journal={arXiv preprint arXiv:1805.00123},
year={2018}
}
Shuai Shao
Megvii Inc.
Zijian Zhao
Beihang University
Boxun Li
Megvii Inc.
Tete Xiao
Peking University
Gang Yu
Megvii Inc.
Xiangyu Zhang
Megvii Inc.
Jian Sun
Megvii Inc.