JHU-CROWD++
2D Box
Classification
Person
|...
许可协议: Custom

Overview

A large-scale unconstrained crowd counting dataset.

A comprehensive dataset with 4,372 images and 1.51 million annotations. In comparison to existing datasets, the proposed dataset is collected under a variety of diverse scenarios and environmental conditions. In addition, the dataset provides comparatively richer set of annotations like dots, approximate bounding boxes, blur levels, etc.

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Label Distribution

Citation

Please use the following citation when referencing the dataset:

@inproceedings{sindagi2019pushing,
title={Pushing the frontiers of unconstrained crowd counting: New dataset and benchmark method},
author={Sindagi, Vishwanath A and Yasarla, Rajeev and Patel, Vishal M},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={1221--1231},
year={2019}
}
@article{sindagi2020jhu-crowd++,
title={JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method},
author={Sindagi, Vishwanath A and Yasarla, Rajeev and Patel, Vishal M},
journal={Technical Report},
year={2020}
}

License

Custom

数据概要
数据格式
Image,
数据量
4.373K
文件大小
2.87GB
发布方
JHU-VIU lab
The Vision & Image Understanding (VIU) Lab is a part of the Electrical and Computer Engineering department in Johns Hopkins University. We focus on several theoretical and application aspects of computer vision and image understanding.
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