Dataset Overview

The dataset provides videos, video frames, and ground truth, therefore offering a superiorly comprehensive dataset for the area of crowd understanding. The sequences are diverse, representing dense crowd in the public spaces in various scenarios such as i-park, school, station, marathon, civic center and basketball court.


  • Type of annotations
    • Head point
    • Head box
    • Visible body box
    • Full body box
    • Face orientation
    • Vehicle visible box
    • Person ID
    • Fine-grained attributes
  • Diversity
    • Various scenarios
    • Good/medium weather conditions
    • Different crowd densities
    • Different levels of occlusion
  • Volume
    • Image size up to 32609 x 24457 pixels
    • 104,196 head points, 121,027 bounding boxes in our initially released dataset


All of the Gigapixel Video Dataset on this page are copyright by Smart Imaging Laboratory, Tsinghua-Berkeley Shenzhen Institute and published under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license.

This dataset is for non-commercial use only. However, if you find yourself or your personal belongings in the data, please contact us, and we will immediately remove the respective images from our servers.


To download the PANDA dataset, please agree on the license and provide the below information via email. We will only take applications from organization email (please DO NOT use the emails from gmail/163/qq). Anyone who uses the PANDA dataset should obey the license and send us an email for registration.

Please use the following email template:

To: zhang-xy18@mails.tsinghua.edu.cn
Subject: Apply for Using PANDA Dataset

I am aware of PANDA Terms of Use and I confirm to comply with it.



When using our datasets in your research, please cite:

title={Multiscale gigapixel video: A cross resolution image matching and warping approach},
author={Yuan, Xiaoyun and Fang, Lu and Dai, Qionghai and Brady, David J and Liu, Yebin},
booktitle={Computational Photography (ICCP), 2017 IEEE International Conference on},