JHU-CROWD++: 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.

JHU-CROWD++: License

This dataset is for academic and non-commercial uses (such as academic research, teaching, scientific publications, or personal experimentation). All images of the JHU-CROWD++ are obtained from the Internet which are not property of VIU-Lab, The Johns Hopkins University (JHU). please contact us if you find yourself or your personal belongings in the data, and we (VIU-Lab) will immediately remove the concerned images from our servers. By downloading and/or using the dataset, you acknowledge that you have read, understand, and agree to be bound by the following terms and conditions.

1. All images are obtained from the Internet. We are not responsible for the content/meaning of these images.
2. Specific care has been taken to reduce labeling errors. Nevertheless, we do not accept any responsibility for errors or omissions.
3. You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
4. You agree not to use the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
5. All rights not expressly granted to you are reserved by us (VIU-Lab, JHU).
6. You acknowledge that the dataset is a valuable scientific resource and agree to appropriately reference the following papers in any publication making use of the Data & Software:
Sindagi et al., "Pushing the frontiers of unconstrained crowd counting: New dataset and benchmark method", ICCV 2019.
Sindagi et al., "JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method", Technical Report 2020.