VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
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...
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| creator | 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 Bouchali, Hadia Nesma 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 Sultan Daud Khan 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 |
| description | 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/}. |
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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. 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| language | eng |
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| subjects | Algorithms Clutter Computer vision Datasets Websites |
| title | VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results |
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