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Vehicle Class Recognition Using Multiple Video Cameras

  • Dongjin Han
  • Jae Hwang
  • Hern-soo Hahn
  • David B. Cooper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

We present an approach to 3D vehicle class recognition (which of SUV, mini-van, sedan, pickup truck) with one or more fixed video-cameras in arbitrary positions with respect to a road. The vehicle motion is assumed to be straight. We propose an efficient method of Structure from Motion (SfM) for camera calibration and 3D reconstruction. 3D geometry such as vehicle and cabin length, width, height, and functions of these are computed and become features for use in a classifier. Classification is done by a minimum probability of error recognizer. Finally, when additional video clips taken elsewhere are available, we design classifiers based on two or more video clips, and this results in significant classification-error reduction.

Keywords

Recognition Rate Video Clip Cross Ratio Structure From Motion Vehicle Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dongjin Han
    • 1
  • Jae Hwang
    • 2
  • Hern-soo Hahn
    • 1
  • David B. Cooper
    • 3
  1. 1.Soongsil UniversitySeoulKorea
  2. 2.The George Washington UniversityUSA
  3. 3.Brown UniversityUSA

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