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Traffic Observation and Situation Assessment

  • Conference paper
Outdoor and Large-Scale Real-World Scene Analysis

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7474))

Abstract

Utilization of camera systems for surveillance tasks (e. g. traffic monitoring) has become a standard procedure and has been in use for over 20 years. However, most of the cameras are operated locally and data analyzed manually. Locally means here a limited field of view and that the image sequences are processed independently from other cameras. For the enlargement of the observation area and to avoid occlusions and non-accessible areas multiple camera systems with overlapping and non-overlapping cameras are used. The joint processing of image sequences of a multi-camera system is a scientific and technical challenge. The processing is divided traditionally into camera calibration, object detection, tracking and interpretation. The fusion of information from different cameras is carried out in the world coordinate system. To reduce the network load, a distributed processing concept can be implemented.

Object detection and tracking are fundamental image processing tasks for scene evaluation. Situation assessments are based mainly on characteristic local movement patterns (e.g. directions and speed), from which trajectories are derived. It is possible to recognize atypical movement patterns of each detected object by comparing local properties of the trajectories. Interaction of different objects can also be predicted with an additional classification algorithm.

This presentation discusses trajectory based recognition algorithms for atypical event detection in multi object scenes to obtain area based types of information (e.g. maps of speed patterns, trajectory curvatures or erratic movements) and shows that two-dimensional areal data analysis of moving objects with multiple cameras offers new possibilities for situational analysis.

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Author information

Authors and Affiliations

  1. Computer Vision, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany

    Ralf Reulke

  2. Institute of Robotics and Mechatronics, DLR German Aerospace Center, Germany

    Dominik Rueß & Kristian Manthey

  3. Institute of Transportation Systems, DLR German Aerospace Center, Rutherfordstr. 2, 12489, Berlin, Germany

    Andreas Luber

Authors
  1. Ralf Reulke
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  2. Dominik Rueß
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  3. Kristian Manthey
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  4. Andreas Luber
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Editor information

Editors and Affiliations

  1. College of Computing Building, Georgia Institute of Technology, 801 Atlantic Drive, 30332-0280, Atlanta, GA, USA

    Frank Dellaert

  2. Department of Computer Science, University of North Carolina at Chapel Hill, Brooks Computer Science Building, CB#3175, 27599, Chapel Hill, NC, USA

    Jan-Michael Frahm

  3. CVG - Institute of Visual Computing, Department of Computer Science, ETH Zurich, CNB G105, Universitaetstrasse 6, 8092, Zurich, Switzerland

    Marc Pollefeys

  4. Institute for Information Processing (TNT), Leibniz Universität, Appelstr. 9A, 30167, Hannover, Germany

    Laura Leal-Taixé

  5. Institute for Information Processing (TNT), Leibniz Universität, Appelstr. 9A, 30167, Hannover, Germany

    Bodo Rosenhahn

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Reulke, R., Rueß, D., Manthey, K., Luber, A. (2012). Traffic Observation and Situation Assessment. In: Dellaert, F., Frahm, JM., Pollefeys, M., Leal-Taixé, L., Rosenhahn, B. (eds) Outdoor and Large-Scale Real-World Scene Analysis. Lecture Notes in Computer Science, vol 7474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34091-8_19

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  • DOI: https://doi.org/10.1007/978-3-642-34091-8_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34090-1

  • Online ISBN: 978-3-642-34091-8

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