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Analysis of the HOG Parameter Effect on the Performance of Vision-Based Vehicle Detection by Support Vector Machine Classifier

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Advances in Parallel and Distributed Computing and Ubiquitous Services

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 368))

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Abstract

Support Vector Machine (SVM) classifier with Histogram of Orientated Gradients (HOG) feature is one of the most popular techniques used for vehicle detection in recent years. In this paper, we study the effect of HOG parameter values on the performance and computing time of vehicle detection. The aim of this paper is to explore the relationship between performance/computing time and HOG parameter values, and eventually to guide finding the most appropriate parameter set to meet specific problem constrains.

This work was supported by the Center for Integrated Smart Sensors funded by the Ministry of Science, ICT and Future Planning as “Global Frontier Project” (CISS-2011-0031863).

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References

  1. Arrospide J, Salgado L, Camplani M (2013) Image-based on-road vehicle detection using cost-effective histograms of oriented gradients. J Vis Commun Image R 24:1182–1190

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Author information

Authors and Affiliations

  1. School of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea

    Kang Yi & Seok-Il Oh

  2. School of Electrical Engineering, Kookmin University, Seoul, Republic of Korea

    Kyeong-Hoon Jung

Authors
  1. Kang Yi
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  2. Seok-Il Oh
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  3. Kyeong-Hoon Jung
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Corresponding author

Correspondence to Kang Yi .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engg., Seoul University of Science & Technology, Seoul, Korea (Republic of)

    James J. (Jong Hyuk) Park

  2. Computer Science & Engineering, Gangneung-Wonju National University, Wonju, Korea (Republic of)

    Gangman Yi

  3. Multimedia Engineering, Dongguk University, Seoul, Korea (Republic of)

    Young-Sik Jeong

  4. School of Computer Science, University of Adelaide, ADELAIDE, South Australia, Australia

    Hong Shen

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© 2016 Springer Science+Business Media Singapore

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Yi, K., Oh, SI., Jung, KH. (2016). Analysis of the HOG Parameter Effect on the Performance of Vision-Based Vehicle Detection by Support Vector Machine Classifier. In: Park, J., Yi, G., Jeong, YS., Shen, H. (eds) Advances in Parallel and Distributed Computing and Ubiquitous Services. Lecture Notes in Electrical Engineering, vol 368. Springer, Singapore. https://doi.org/10.1007/978-981-10-0068-3_29

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  • DOI: https://doi.org/10.1007/978-981-10-0068-3_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0067-6

  • Online ISBN: 978-981-10-0068-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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