  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.
