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Detection and Negotiation of Obstacles

The common approach towards the detection of obstacles searches for cues which are incompatible with the assumption that a certain area of the image corresponds to a road surface: such cues could be significant gray value transitions, texture boundaries, or - alternatively - regions with unexpected gray values or colors. Although these approaches are relatively cheap regarding the necessary computing power, usually their detection rate is low or their false alarm rate is high.

More reliable are approaches which essentially exploit the phenomenon that anything extending from the - assumed planar - road will exhibit a disparity which differs from that of points on the road plane itself if image frames recorded from different vantage points are compared. So far, all such approaches assume that the external camera parameters are known. Three types of approaches can be distinguished here, depending on whether the image frames based on which the disparity is estimated are recorded

  1. at the same time by a stereo camera set-up,
  2. by a single camera with a finite temporal difference between consecutive image frames or
  3. at close to infinitesimal time intervals (i. e. at full video rate).
In the first case, the known relative calibration of the stereo-setup is exploited in order to warp one image onto the other in the image area assumed to correspond to free road space in front of the vehicle and to compare the gray values - or image features extracted from them - which are mapped onto each other by the interframe warping process. If the road surface is planar and free, mapping from one stereo frame to the other should associate compatible gray or feature values. Thresholding the difference, aggregation of image areas with significant differences and comparison of the resulting difference areas with some minimum size threshold characterize the basic steps for obstacle detection in this case. As an alternative to explicit calculation of the left-to-right warp transformation, image features are extracted and put into correspondence between the two stereo frames in order to determine the disparity. The threshold is then applied to the disparity estimate. In case (2) above, the relative calibration of a stereo camera pair is replaced by knowledge about motion of a monocular camera with respect to the road between two recorded image frames. Both approaches (explicit calculation of the expected shear mapping or the determination of a disparity value between corresponding image locations) sketched for the comparison of stereo image frames can then be applied to this case, too.

The approach mentioned above under (3) differs from the disparity variant of approach (2) only insofar as the optical flow is estimated instead of the displacement between corresponding image locations from two frames taken some time apart. Optical flow estimation is still time consuming. If sufficient computing power is available, it has the advantage of being performed by a non-search calculation restricted to a local spatio-temporal (x,y,t)-volume from the recorded monocular gray value stream. Even non-prominent texture or gradual gray value transitions such as those due to illumination gradients can be exploited in this manner, resulting in a more densely populated optical flow field. Feature-based approaches towards the estimation of optical flow usually result in less densely populated optical flow fields which increase the difficulty to reliably segment the image area corresponding to a potential obstacle.

The advantage of all these approaches - versus those based on the detection of unexpected gray value configurations in the image area associated with the road surface in front of the vehicle - consists in the fact that knowledge about the geometry of a projective transformation from one view to another can be exploited. Even high contrast marks or shadows on the road surface can thus be easily distinguished from objects extending vertically from the road plane. Special purpose processor arrangements already allow to compute optical flow fields or warping transformations in real time although the resolution and reliability still leaves ample room for improvement.

next up previous
Next: Detection and Tracking of Up: Discussion of CV for Previous: Necessity for a Transition