VisDrone-CC2021: The Vision Meets Drone Crowd Counting Challenge Results

Zhihao Liu, Zhijian He, Lujia Wang, Wenguan Wang, Yixuan Yuan, Dingwen Zhang, Jinglin Zhang, Pengfei Zhu, Luc Van Gool, Junwei Han, Steven Hoi, Qinghua Hu, Ming Liu, Junwen Pan; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 2830-2838

Crowding counting research evolves quickly by the leverage of development in deep learning. Many researchers put their efforts into crowd counting tasks and have achieved many significant improvements. However, current datasets still barely satisfy this evolution and high quality evaluation data is urgent. Motivated by high quality and quantity study in crowding counting, we collect a drone-captured dataset formed by 5,468 images(images in RGB and thermal appear in pairs and 2,734 respectively). There are 1,807 pairs of images for training, and 927 pairs for testing. We manually annotate persons with points in each frame. Based on this dataset, we organized the Vision Meets Drone Crowd Counting Challenge(Visdrone-CC2021) in conjunction with the International Conference on Computer Vision (ICCV 2021). Our challenge attracts many researchers to join, which pave the road of speed up the milestone in crowding counting. To summarize the competition, we select the most remarkable algorithms from participants' submissions and provide a detailed analysis of the evaluation results. More information can be found at the website: http://www.aiskyeye.com/.