학술논문
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Document Type
Working Paper
Author
Du, Dawei; Wen, Longyin; Zhu, Pengfei; Fan, Heng; Hu, Qinghua; Ling, Haibin; Shah, Mubarak; Pan, Junwen; Al-Ali, Ali; Mohamed, Amr; Imene, Bakour; Dong, Bin; Zhang, Binyu; Nesma, Bouchali Hadia; Xu, Chenfeng; Duan, Chenzhen; Castiello, Ciro; Mencar, Corrado; Liang, Dingkang; Krüger, Florian; Vessio, Gennaro; Castellano, Giovanna; Wang, Jieru; Gao, Junyu; Abualsaud, Khalid; Ding, Laihui; Zhao, Lei; Cianciotta, Marco; Saqib, Muhammad; Almaadeed, Noor; Elharrouss, Omar; Lyu, Pei; Wang, Qi; Liu, Shidong; Qiu, Shuang; Pan, Siyang; Al-Maadeed, Somaya; Khan, Sultan Daud; Khattab, Tamer; Han, Tao; Golda, Thomas; Xu, Wei; Bai, Xiang; Xu, Xiaoqing; Li, Xuelong; Zhao, Yanyun; Tian, Ye; Lin, Yingnan; Xu, Yongchao; Yao, Yuehan; Xu, Zhenyu; Zhao, Zhijian; Luo, Zhipeng; Wei, Zhiwei; Zhao, Zhiyuan
Source
European Conference on Computer Vision. Springer, Cham, 2020: 675-691
Subject
Language
Abstract
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint. However, there are few algorithms focusing on crowd counting on the drone-captured data due to the lack of comprehensive datasets. To this end, we collect a large-scale dataset and organize the Vision Meets Drone Crowd Counting Challenge (VisDrone-CC2020) in conjunction with the 16th European Conference on Computer Vision (ECCV 2020) to promote the developments in the related fields. The collected dataset is formed by $3,360$ images, including $2,460$ images for training, and $900$ images for testing. Specifically, we manually annotate persons with points in each video frame. There are $14$ algorithms from $15$ institutes submitted to the VisDrone-CC2020 Challenge. We provide a detailed analysis of the evaluation results and conclude the challenge. More information can be found at the website: \url{http://www.aiskyeye.com/}.
Comment: The method description of A7 Mutil-Scale Aware based SFANet (M-SFANet) is updated and missing references are added
Comment: The method description of A7 Mutil-Scale Aware based SFANet (M-SFANet) is updated and missing references are added