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Learning Non-coplanar Scene Models by Exploring the Height Variation of Tracked Objects

  • Fei Yin
  • Dimitrios Makris
  • James Orwell
  • Sergio A. Velastin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

In this paper, we present a novel method to overcome the common constraint of traditional camera calibration methods of surveillance systems where all objects move on a single coplanar ground plane. The proposed method estimates a scene model with non-coplanar planes by measuring the variation of pedestrian heights across the camera FOV in a statistical manner. More specifically, the proposed method automatically segments the scene image into plane regions, estimates a relative depth and estimates the altitude for each image pixel, thus building up a 3D structure with multiple non-coplanar planes. By being able to estimate the non-coplanar planes, the method can extend the applicability of 3D (single or multiple camera) tracking algorithms to a range of environments where objects (pedestrians and/or vehicles) can move on multiple non-coplanar planes (e.g. multiple levels, overpasses and stairs).

Keywords

Camera calibration non-coplanar planes region segmentation motion variety depth and altitude estimation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fei Yin
    • 1
  • Dimitrios Makris
    • 1
  • James Orwell
    • 1
  • Sergio A. Velastin
    • 1
  1. 1.Digital Imaging Research Centre, Faculty of Computing, Information Systems and MathematicsKingston UniversityKingston upon ThamesUnited Kingdom

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