The authors outline a selective analysis approach to motion estimation that promises to provide the precision and efficiency required for autonomous vehicle guidance. Efficiency is achieved by implementing computations within a hierarchical (pyramid) structure, and by restricting these computations to selected regions of the scene. These analysis regions are moved dynamically over the scene as a sequence of focal probes, much as a human driver moves his or her eyes and shifts visual attention. Precise motion estimates are obtained by fitting models comprising one or two rigidly moving surfaces to the image data within each focal analysis region. Differential motion within the region separates foreground from background objects, while overall region motion relative to the focus of expansion determines distance from the observer. High level intelligent control directs the focal probes. It is shown through examples that model-based motion estimation can be used to detect obstacles in the road, and to discriminate such obstacles from road markings. High level intelligent control is described briefly.
Dynamic analysis of image motion for vehicle guidance
Dynamische Bildanalyse bei Fahrzeugbewegungen mit einer autonomen Führung
1990
, 5 Quellen
vol.1
Conference paper
English
FAHRZEUGFUEHRUNG , AUTOMATISCHE STEUERUNG , BILDANALYSE , BILDERKENNUNG , DYNAMISCHES SYSTEM , PARAMETERSCHAETZUNG , RECHNERUNTERSTUETZUNG , MEHRDIMENSIONALE STRUKTUR , ZIELERKENNUNG , SYSTEMBESCHREIBUNG , INDUSTRIEROBOTER , MASCHINELLES SEHEN , DIGITALE BILDVERARBEITUNG , DIGITALE BILDERKENNUNG , MOBILER ROBOTER , DIFFERENZIALBEWEGUNG , VEHICLE GUIDANCE , IMAGE MOTION