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International Conference on Computer Analysis of Images and Patterns

CAIP 1995: Computer Analysis of Images and Patterns pp 874–879Cite as

Direct obstacle detection and motion from spatio-temporal derivatives

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Part of the book series: Lecture Notes in Computer Science ((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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Authors and Affiliations

  1. Computational Vision and Active Perception Laboratory (CVAP) Department of Numerical Analysis and Computing Science, Royal Institute of Technology, S-100 44, Stockholm, Sweden

    Pär Fornland

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  1. Pär Fornland
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Václav Hlaváč Radim Šára

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© 1995 Springer-Verlag Berlin Heidelberg

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Fornland, P. (1995). Direct obstacle detection and motion from spatio-temporal derivatives. In: Hlaváč, V., Šára, R. (eds) Computer Analysis of Images and Patterns. CAIP 1995. Lecture Notes in Computer Science, vol 970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60268-2_396

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  • DOI: https://doi.org/10.1007/3-540-60268-2_396

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60268-2

  • Online ISBN: 978-3-540-44781-8

  • eBook Packages: Springer Book Archive

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