Direct Obstacle Detection and Motion from Spatio-Temporal Derivatives
Proc. 6th International Conference on Computer Analysis of Images and Patterns
(Prague, Chech Republic), September 1995.
Autonomous vehicles need a means of detecting obstructions on its
path, to avoid collision. In this paper, a novel approach to obstacle
detection is presented. A camera moves on a visible ground plane
with the optical axis parallel to the ground.
Camera motion parameters are linearly related to first order
spatio-temporal derivatives of the taken image sequence; image flow
is not neenada MotionCVAP/ is robustly estimated using RANSAC. An error
measure for each image point corresponds to the likelihood of an obstacle
in that point.
obstacle detection, motion estimation, optical flow,
computer vision, autonomous vehicles