Estimating Surrounding Vehicles' Pose using Computer Vision
Paper i proceeding, 2010
This paper presents a computer vision-based approach to tracking surrounding vehicles and estimating their
trajectories, in order to detect potentially dangerous situations.
Images are acquired using a camera mounted in the ego vehicle.
Estimations of the distance, velocity and orientation of other
vehicles on the road are obtained by detecting their lights
and shadow. Because 3D information is not readily available
in a mono-camera system, several sets of constraints and
assumptions on the geometry of both road and vehicles are
proposed and tested in this paper. Kalman ﬁlters are used
to track the detected vehicles. We also study the advantages
of tracking the vehicles in road space (world coordinates), or
tracking the position of the lights and shadows on the image.
The performance of the different approaches is evaluated on
video recorded in an urban environment.