Novel vehicle detection system based on stacked DoG kernel and AdaBoost

Hyun Ho Kang; Seo Won Lee; Sung Hyun You; Choon Ki Ahn

doi:10.1371/journal.pone.0193733

Novel vehicle detection system based on stacked DoG kernel and AdaBoost

Hyun Ho Kang, Seo Won Lee, Sung Hyun You, Choon Ki Ahn

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

This paper proposes a novel vehicle detection system that can overcome some limitations of typical vehicle detection systems using AdaBoost-based methods. The performance of the AdaBoost-based vehicle detection system is dependent on its training data. Thus, its performance decreases when the shape of a target differs from its training data, or the pattern of a preceding vehicle is not visible in the image due to the light conditions. A stacked Difference of Gaussian (DoG)–based feature extraction algorithm is proposed to address this issue by recognizing common characteristics, such as the shadow and rear wheels beneath vehicles—of vehicles under various conditions. The common characteristics of vehicles are extracted by applying the stacked DoG shaped kernel obtained from the 3D plot of an image through a convolution method and investigating only certain regions that have a similar patterns. A new vehicle detection system is constructed by combining the novel stacked DoG feature extraction algorithm with the AdaBoost method. Experiments are provided to demonstrate the effectiveness of the proposed vehicle detection system under different conditions.

Original language	English
Article number	e0193733
Journal	PloS one
Volume	13
Issue number	3
DOIs	https://doi.org/10.1371/journal.pone.0193733
Publication status	Published - 2018 Mar

Bibliographical note

Funding Information:
This work was supported partially by the NRF through the Ministry of Science, ICT, and Future Planning under Grant NRF-2017R1A1A1A05001325, and partially by “Human Resources program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Requblic of Korea (No. 20174030201820). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was supported partially by the NRF through the Ministry of Science, ICT, and Future Planning under Grant NRF-2017R1A1A1A05001325, and partially by “Human Resources program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Requblic of Korea (No. 20174030201820). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Publisher Copyright:
© 2018 Kang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ASJC Scopus subject areas

General Biochemistry,Genetics and Molecular Biology
General Agricultural and Biological Sciences
General

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10.1371/journal.pone.0193733

Cite this

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title = "Novel vehicle detection system based on stacked DoG kernel and AdaBoost",

abstract = "This paper proposes a novel vehicle detection system that can overcome some limitations of typical vehicle detection systems using AdaBoost-based methods. The performance of the AdaBoost-based vehicle detection system is dependent on its training data. Thus, its performance decreases when the shape of a target differs from its training data, or the pattern of a preceding vehicle is not visible in the image due to the light conditions. A stacked Difference of Gaussian (DoG)–based feature extraction algorithm is proposed to address this issue by recognizing common characteristics, such as the shadow and rear wheels beneath vehicles—of vehicles under various conditions. The common characteristics of vehicles are extracted by applying the stacked DoG shaped kernel obtained from the 3D plot of an image through a convolution method and investigating only certain regions that have a similar patterns. A new vehicle detection system is constructed by combining the novel stacked DoG feature extraction algorithm with the AdaBoost method. Experiments are provided to demonstrate the effectiveness of the proposed vehicle detection system under different conditions.",

author = "Kang, {Hyun Ho} and Lee, {Seo Won} and You, {Sung Hyun} and Ahn, {Choon Ki}",

note = "Funding Information: This work was supported partially by the NRF through the Ministry of Science, ICT, and Future Planning under Grant NRF-2017R1A1A1A05001325, and partially by “Human Resources program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Requblic of Korea (No. 20174030201820). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was supported partially by the NRF through the Ministry of Science, ICT, and Future Planning under Grant NRF-2017R1A1A1A05001325, and partially by “Human Resources program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Requblic of Korea (No. 20174030201820). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript Publisher Copyright: {\textcopyright} 2018 Kang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",

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N2 - This paper proposes a novel vehicle detection system that can overcome some limitations of typical vehicle detection systems using AdaBoost-based methods. The performance of the AdaBoost-based vehicle detection system is dependent on its training data. Thus, its performance decreases when the shape of a target differs from its training data, or the pattern of a preceding vehicle is not visible in the image due to the light conditions. A stacked Difference of Gaussian (DoG)–based feature extraction algorithm is proposed to address this issue by recognizing common characteristics, such as the shadow and rear wheels beneath vehicles—of vehicles under various conditions. The common characteristics of vehicles are extracted by applying the stacked DoG shaped kernel obtained from the 3D plot of an image through a convolution method and investigating only certain regions that have a similar patterns. A new vehicle detection system is constructed by combining the novel stacked DoG feature extraction algorithm with the AdaBoost method. Experiments are provided to demonstrate the effectiveness of the proposed vehicle detection system under different conditions.

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Novel vehicle detection system based on stacked DoG kernel and AdaBoost

Abstract

Bibliographical note

ASJC Scopus subject areas

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