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Vision-Based Vehicle Counting with High Accuracy for Highways with Perspective View

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Book cover Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9475))

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Abstract

Vehicle detection by motion is still a common method used in vision-based tracking systems due to vehicles’ continuous motion on highways. However, counting accuracy is affected for highways with perspective view due to long-time merging (i.e. blob merging or occlusion) events. In this work, a new way of vehicle counting with high accuracy using two appearance-based classifiers is proposed to detect merging situations and handle vehicle counts. Experimental results on three Las Vegas highways with differing perspective views and congestion difficulties show improvement in counting and general applicability of the proposed method. Moreover, tracking and counting results of a highly cluttered highway indicates greater counting improvement (89 % to 94 %) for highly congested situations.

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Author information

Authors and Affiliations

  1. University of Nevada, Las Vegas, USA

    Mohammad Shokrolah Shirazi & Brendan Morris

Authors
  1. Mohammad Shokrolah Shirazi
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  2. Brendan Morris
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Corresponding author

Correspondence to Mohammad Shokrolah Shirazi .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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© 2015 Springer International Publishing Switzerland

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Shirazi, M.S., Morris, B. (2015). Vision-Based Vehicle Counting with High Accuracy for Highways with Perspective View. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_76

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_76

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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