Annual Report 2010 - Fachgruppe Informatik an der RWTH Aachen ...

Level-Set Tracking for Automotive Applications
Esther Horbert, Dennis Mitzel, Bastian Leibe
Object tracking from a mobile platform is an important problem for many applications as for
example driver assistance systems. Taking as input the video streams from a stereo camera
pair mounted on a moving vehicle, our goal is to track other traffic participants for dynamic
scene analysis. This is highly challenging since both the cameras and other vehicles can move
very quickly through an unknown and complex environment with changing illumination and
weather conditions.
In this project, we are working on the integration of geometric scene knowledge into a levelset
tracking framework. We use a level-set tracker to track an object's shape, in this case a
car's trunk, and estimate not only its displacement, but also how much the car rotated between
the two video frames. Our approach is based on a novel constrained-homography
transformation model that restricts the deformation space to physically plausible rigid motion
on the ground plane. This model is especially suitable for tracking vehicles in automotive
scenarios. Apart from reducing the number of parameters in the estimation, the 3D
transformation model allows us to obtain additional information about the tracked objects and
to recover their detailed 3D motion and orientation at every time step. This information is fed
to a higher-level tracker which associates the tracked positions over time and produces car
trajectories that are consistent with the scene. The orientation estimate is here used to improve
a Kalman Filter estimate of the tracked vehicle dynamics. This leads to more accurate object
trajectories, which in turn enables the system to make more accurate predictions about the
future positions of the tracked vehicle.
291