We present PANDA, 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 scenes with both wide field-of-view (~1 square kilometer area) and high-resolution details (~gigapixel-level/frame). The scenes may contain 4k head counts with over 100x 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. We benchmark the human detection and tracking tasks. Due to the vast variance of pedestrian pose, scale, occlusion and trajectory, existing approaches are challenged by both accuracy and efficiency. Given the uniqueness of PANDA with both wide FoV and high resolution, a new task of interaction-aware group detection is introduced. We design a 'global-to-local zoom-in' framework, where global trajectories and local interactions are simultaneously encoded, yielding promising results. We believe PANDA will contribute to the community of artificial intelligence and praxeology by understanding human behaviors and interactions in large-scale real-world scenes. PANDA Website: http://www.panda-dataset.com.
@article{arxiv.2003.04852,
title = {PANDA: A Gigapixel-level Human-centric Video Dataset},
author = {Xueyang Wang and Xiya Zhang and Yinheng Zhu and Yuchen Guo and Xiaoyun Yuan and Liuyu Xiang and Zerun Wang and Guiguang Ding and David J Brady and Qionghai Dai and Lu Fang},
journal= {arXiv preprint arXiv:2003.04852},
year = {2020}
}
Comments
Accepted by IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020