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Bicycle Detection Using HOG, HSC and MLBP

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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

Due to the growing number of bicycles on roads, safety of bicyclists is drawing the increasing attention of transportation departments. Intelligent Transportation Systems (ITS) use automated tools for processing and analysis of traffic video data to plan and implement safety measures. One of important factors that influence the planning and safety countermeasures for bicyclists is the bicycle count. In this paper, we develop a bicycle detection method that can be used in a bicycle counting system. We strive to improve the efficiency of detection by looking for classification features that deliver more versatile information to automatic classifiers. We explore a combination of Histograms of Oriented Gradients (HOG), Histogram of Shearlet Coefficients (HSC) and Multi-scale Local Binary Pattern (MLBP) to improve detection and count of bicycles in video data. It is shown that the combination of the above features secures a higher detection accuracy.

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Author information

Authors and Affiliations

  1. Electrical and Computer Engineering Department, University of Nevada, Las Vegas, NV, 89154, USA

    Farideh Foroozandeh Shahraki, Ali Pour Yazdanpanah, Emma E. Regentova & Venkatesan Muthukumar

Authors
  1. Farideh Foroozandeh Shahraki
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  2. Ali Pour Yazdanpanah
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  3. Emma E. Regentova
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  4. Venkatesan Muthukumar
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Corresponding author

Correspondence to Ali Pour Yazdanpanah .

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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Shahraki, F.F., Yazdanpanah, A.P., Regentova, E.E., Muthukumar, V. (2015). Bicycle Detection Using HOG, HSC and MLBP. 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_51

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_51

  • 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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