1 research outputs found
Vision Meets Drones: Past, Present and Future
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 evelopments of object detection and
tracking algorithms, we have organized two challenge workshops in conjunction
with ECCV 2018, and ICCV 2019, 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
presents 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 on 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 the website:
https://github.com/VisDrone/VisDrone-Dataset.Comment: arXiv admin note: text overlap with arXiv:1804.0743