Abstract:
Intelligent vehicle systems need to distinguish which objects
are moving and which are static. A static concrete wall lying in the path
of a vehicle should be treated differently than a truck moving in front
of the vehicle. This paper proposes a new algorithm that addresses this
problem, by providing dense dynamic depth information, while coping
with real-time constraints. The algorithm models disparity and disparity
rate pixel-wise for an entire image. This model is integrated over time and
tracked by means of many pixel-wise Kalman filters. This provides better
depth estimation results over time, and also provides speed information
at each pixel without using optical flow. This simple approach leads
to good experimental results for real stereo sequences, by showing an
improvement over previous methods.
Description:
You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the original MI_tech website http://www.mi.auckland.ac.nz/index.php?option=com_content&view=article&id=91&Itemid=76 . All other rights are reserved by the author(s).