  Drones, or general UAVs, equipped with cameras have been fast deployed with a
wide range of applications, including agriculture, aerial photography, and
surveillance. Consequently, automatic understanding of visual data collected
from drones becomes highly demanding, bringing computer vision and drones more
and more closely. To promote and track the developments of object detection and
tracking algorithms, we have organized three challenge workshops in conjunction
with ECCV 2018, ICCV 2019 and ECCV 2020, attracting more than 100 teams around
the world. We provide a large-scale drone captured dataset, VisDrone, which
includes four tracks, i.e., (1) image object detection, (2) video object
detection, (3) single object tracking, and (4) multi-object tracking. In this
paper, we first present a thorough review of object detection and tracking
datasets and benchmarks, and discuss the challenges of collecting large-scale
drone-based object detection and tracking datasets with fully manual
annotations. After that, we describe our VisDrone dataset, which is captured
over various urban/suburban areas of 14 different cities across China from
North to South. Being the largest such dataset ever published, VisDrone enables
extensive evaluation and investigation of visual analysis algorithms for the
drone platform. We provide a detailed analysis of the current state of the
field of large-scale object detection and tracking on drones, and conclude the
challenge as well as propose future directions. We expect the benchmark largely
boost the research and development in video analysis on drone platforms. All
the datasets and experimental results can be downloaded from
https://github.com/VisDrone/VisDrone-Dataset.
