  Satellites are capable of capturing high-resolution videos. It makes vehicle
perception from satellite become possible. Compared to street surveillance,
drive recorder or other equipments, satellite videos provide a much broader
city-scale view, so that the global dynamic scene of the traffic are captured
and displayed. Traffic monitoring from satellite is a new task with great
potential applications, including traffic jams prediction, path planning,
vehicle dispatching, \emph{etc.}. Practically, limited by the resolution and
view, the captured vehicles are very tiny (a few pixels) and move slowly. Worse
still, these satellites are in Low Earth Orbit (LEO) to capture such
high-resolution videos, so the background is also moving. Under this
circumstance, traffic monitoring from the satellite view is an extremely
challenging task. To attract more researchers into this field, we build a
large-scale benchmark for traffic monitoring from satellite. It supports
several tasks, including tiny object detection, counting and density
estimation. The dataset is constructed based on 12 satellite videos and 14
synthetic videos recorded from GTA-V. They are separated into 408 video clips,
which contain 7,336 real satellite images and 1,960 synthetic images. 128,801
vehicles are annotated totally, and the number of vehicles in each image varies
from 0 to 101. Several classic and state-of-the-art approaches in traditional
computer vision are evaluated on the datasets, so as to compare the performance
of different approaches, analyze the challenges in this task, and discuss the
future prospects. The dataset is available at:
https://github.com/Chenxi1510/Vehicle-Perception-from-Satellite-Videos.
