VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
This addresses the problem of limited data for researchers in computer vision working on drone crowd counting, though it is incremental as it primarily provides a new dataset.
The authors tackled the lack of datasets for drone-based crowd counting by collecting a large-scale dataset of 3,360 images and organizing a challenge, resulting in 14 algorithms submitted and evaluated.
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/}.