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Direct obstacle detection and motion from spatio-temporal derivatives

  • Pär Fornland
Posters
Part of the Lecture Notes in Computer Science book series (LNCS, volume 970)

Abstract

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 needed. Motion is robustly estimated using RANSAC. An error measure for each image point corresponds to the likelihood of an obstacle in that point.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Pär Fornland
    • 1
  1. 1.Computational Vision and Active Perception Laboratory (CVAP) Department of Numerical Analysis and Computing ScienceRoyal Institute of TechnologyStockholmSweden

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