JHU-CROWD++
2D Box
Classification
Person
|...
License: 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.

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

Data Summary
Type
Image,
Amount
4.373K
Size
--
Provided by
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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