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International Conference on Computing and Information Technology

IC2IT 2017: Recent Advances in Information and Communication Technology 2017 pp 275–284Cite as

Comparative Study of Computational Time that HOG-Based Features Used for Vehicle Detection

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 566))

Abstract

HOG produces a number of redundant and long features so that they affect to the detection rate and computational time. This paper studied the processes that HOG-based features were generated, selected, and used in vehicle detection and find one that takes the shortest time. There were five combinations of feature extractors and classifiers. Time spent in HV step, accuracy of detection and the false positive rate are considered together for making decision of which combination is the best. The experiments were conducted on GIT dataset. The experimental results showed that process which VHOG preceded ELM provided a little less accurate than HOG preceded SVM did. However, it did not only take shortest time in HV step but also provided the lowest false positive rate. Therefore, VHOG preceded ELM should be selected as a method for vehicle detection.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand

    Natthariya Laopracha & Khamron Sunat

Authors
  1. Natthariya Laopracha
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  2. Khamron Sunat
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Corresponding author

Correspondence to Natthariya Laopracha .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Phayung Meesad

  2. Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Sunantha Sodsee

  3. Lehrgebiet Kommunikationsnetze, FernUniversität in Hagen, Hagen, Germany

    Herwig Unger

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Laopracha, N., Sunat, K. (2018). Comparative Study of Computational Time that HOG-Based Features Used for Vehicle Detection. In: Meesad, P., Sodsee, S., Unger, H. (eds) Recent Advances in Information and Communication Technology 2017. IC2IT 2017. Advances in Intelligent Systems and Computing, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-319-60663-7_26

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  • DOI: https://doi.org/10.1007/978-3-319-60663-7_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60662-0

  • Online ISBN: 978-3-319-60663-7

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