VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results

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/}.

The method description of A7 Mutil-Scale Aware based SFANet (M-SFANet) is updated and missing references are added

References in corpus (8)

• High-Resolution Representations for Labeling Pixels and Regions
• Deep Retinex Decomposition for Low-Light Enhancement
• Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank
• CNN-based Density Estimation and Crowd Counting: A Survey
• Dual Path Multi-Scale Fusion Networks with Attention for Crowd Counting
• Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network
• MRCNet: Crowd Counting and Density Map Estimation in Aerial and Ground Imagery
• Rethinking Object Detection in Retail Stores