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:
http://www.aiskyeye.com/.

本文介绍了无人机平台上群体计数的挑战，以及为了解决数据集的问题，提出了大规模数据集并组织相关挑战赛的方法。

VisDrone-CC2020：无人机行人计数任务挑战的比赛结果

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

In this paper we present a large-scale visual object detection and tracking
benchmark, named VisDrone2018, aiming at advancing visual understanding tasks
on the drone platform. The images and video sequences in the benchmark were
captured over various urban/suburban areas of 14 different cities across China
from north to south. Specifically, VisDrone2018 consists of 263 video clips and
10,209 images (no overlap with video clips) with rich annotations, including
object bounding boxes, object categories, occlusion, truncation ratios, etc.
With intensive amount of effort, our benchmark has more than 2.5 million
annotated instances in 179,264 images/video frames. Being the largest such
dataset ever published, the benchmark enables extensive evaluation and
investigation of visual analysis algorithms on the drone platform. In
particular, we design four popular tasks with the benchmark, including object
detection in images, object detection in videos, single object tracking, and
multi-object tracking. All these tasks are extremely challenging in the
proposed dataset due to factors such as occlusion, large scale and pose
variation, and fast motion. We hope the benchmark largely boost the research
and development in visual analysis on drone platforms.

本篇论文介绍了一个名为 VisDrone2018 的大规模视觉对象检测和跟踪基准，目的在于推进基于无人机平台上的视觉理解任务，其包含了 14 个不同城市的各种城市 / 郊区地区的图像和视频序列，并提供了丰富的标注，如物体边界框的位置，物体类别，遮挡，截断比等，是迄今为止发表的最大的数据集之一，可广泛评估并研究在无人机平台上的视觉分析技术。