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Cascade-Adaboost for Pedestrian Detection Using HOG and Combined Features

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Advances in Computer Science and Ubiquitous Computing (UCAWSN 2016, CUTE 2016, CSA 2016)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 421))

Abstract

Over the recent years, pedestrian detection beings in a video surveillance system is attracting more attention due to its wide range of applications. In this paper, we propose an efficient two-phase pedestrian detector using HOG and combined features. The detector finds pedestrian candidate regions with a cascade-adaboost on HOG features. It then verifies each candidate using a combined features, which is local (SURF) and global features (RGB histogram), and then a classification based on MLP. It obtains a better detection rate and false-positive rate. The pedestrian detection system experimented with PETS 2009 dataset proves the effectiveness of our detection model.

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2014R1A2A1A11053902).

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Author information

Authors and Affiliations

  1. College of Information and Communication Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, Republic of Korea

    Gyujin Jang, Jinhee Park & Moonhyun Kim

Authors
  1. Gyujin Jang
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  2. Jinhee Park
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  3. Moonhyun Kim
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Corresponding author

Correspondence to Moonhyun Kim .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea (Republic of)

    James J. (Jong Hyuk) Park

  2. Department of Computer Science, Georgia State University, Atlanta, Georgia, USA

    Yi Pan

  3. Computer Science and Engineering, Gangneung-Wonju National University, Wonju, Korea (Republic of)

    Gangman Yi

  4. University Salerno, Fisciano, Italy

    Vincenzo Loia

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Jang, G., Park, J., Kim, M. (2017). Cascade-Adaboost for Pedestrian Detection Using HOG and Combined Features. In: Park, J., Pan, Y., Yi, G., Loia, V. (eds) Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016 2016 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-10-3023-9_67

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  • DOI: https://doi.org/10.1007/978-981-10-3023-9_67

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3022-2

  • Online ISBN: 978-981-10-3023-9

  • eBook Packages: EngineeringEngineering (R0)

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