PANDA: A Gigapixel-level Human-centric Video Dataset

Xueyang Wang, Xiya Zhang, Yinheng Zhu, Yuchen Guo, Xiaoyun Yuan, Liuyu Xiang, Zerun Wang, Guiguang Ding, David Brady, Qionghai Dai, Lu Fang
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). 2020.

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PANDA is the first gigaPixel-level humAN-centric viDeo dAtaset, for large-scale, long-term, and multi-object visual analysis. The videos in PANDA were captured by a gigapixel camera and cover real-world large-scale scenes with both wide field-of-view (~1km^2 area) and high resolution details (gigapixel-level/frame). The scenes may contain 4k head counts with over 100× scale variation. PANDA provides enriched and hierarchical ground-truth annotations, including 15,974.6k bounding boxes, 111.8k fine-grained attribute labels, 12.7k trajectories, 2.2k groups and 2.9k interactions.

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If you find this dataset useful for your research, please cite:

@inproceedings{wang2020panda, title={PANDA: A Gigapixel-level Human-centric Video Dataset}, author={Wang, Xueyang and Zhang, Xiya and Zhu, Yinheng and Guo, Yuchen and Yuan, Xiaoyun and Xiang, Liuyu and Wang, Zerun and Ding, Guiguang and Brady, David J and Dai, Qionghai and Fang, Lu}, booktitle={Computer Vision and Pattern Recognition (CVPR), 2020 IEEE International Conference on}, year={2020}, organization={IEEE} }