Human tracking method based on improved HOG+Real AdaBoost

Daisuke Aoki; Junzo Watada

doi:10.1109/ASCC.2015.7244780

Human tracking method based on improved HOG+Real AdaBoost

Daisuke Aoki, Junzo Watada

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This paper proposes an object detection method that uses Histograms of Oriented Gradients (HOG) features using boosting algorithm. There has been done many research works in late years on statistical learning methods and object detection methods that associate low level of features obtained. However the proposed approach, low level of HOG features are associated by using Real AdaBoost to continuously achieve features. In this wise, it is possible to capture a shape of edge continuity, which single HOG features can't do, so highly accuracy detection is realized. This paper, to evaluate the effectiveness of the proposed method, three different experiments with different patterns are conducted for detecting humans. Moreover, a boosting classifier is used to represent the co-occurrence of HOG features appearance for detecting a human.

Original language	English
Title of host publication	2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781479978625
DOIs	https://doi.org/10.1109/ASCC.2015.7244780
Publication status	Published - 2015 Sept 8
Event	10th Asian Control Conference, ASCC 2015 - Kota Kinabalu, Malaysia Duration: 2015 May 31 → 2015 Jun 3

Other

Other	10th Asian Control Conference, ASCC 2015
Country/Territory	Malaysia
City	Kota Kinabalu
Period	15/5/31 → 15/6/3

Keywords

Histograms of Oriented Gradients
Real Adaboost

ASJC Scopus subject areas

Control and Systems Engineering

Access to Document

10.1109/ASCC.2015.7244780

Cite this

Aoki, D & Watada, J 2015, Human tracking method based on improved HOG+Real AdaBoost. in 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015., 7244780, Institute of Electrical and Electronics Engineers Inc., 10th Asian Control Conference, ASCC 2015, Kota Kinabalu, Malaysia, 15/5/31. https://doi.org/10.1109/ASCC.2015.7244780

@inproceedings{510e62dc7b434596a75f5621dc139f90,

title = "Human tracking method based on improved HOG+Real AdaBoost",

abstract = "This paper proposes an object detection method that uses Histograms of Oriented Gradients (HOG) features using boosting algorithm. There has been done many research works in late years on statistical learning methods and object detection methods that associate low level of features obtained. However the proposed approach, low level of HOG features are associated by using Real AdaBoost to continuously achieve features. In this wise, it is possible to capture a shape of edge continuity, which single HOG features can't do, so highly accuracy detection is realized. This paper, to evaluate the effectiveness of the proposed method, three different experiments with different patterns are conducted for detecting humans. Moreover, a boosting classifier is used to represent the co-occurrence of HOG features appearance for detecting a human.",

keywords = "Histograms of Oriented Gradients, Real Adaboost",

author = "Daisuke Aoki and Junzo Watada",

year = "2015",

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day = "8",

doi = "10.1109/ASCC.2015.7244780",

language = "English",

isbn = "9781479978625",

booktitle = "2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "10th Asian Control Conference, ASCC 2015 ; Conference date: 31-05-2015 Through 03-06-2015",

}

TY - GEN

T1 - Human tracking method based on improved HOG+Real AdaBoost

AU - Aoki, Daisuke

AU - Watada, Junzo

PY - 2015/9/8

Y1 - 2015/9/8

N2 - This paper proposes an object detection method that uses Histograms of Oriented Gradients (HOG) features using boosting algorithm. There has been done many research works in late years on statistical learning methods and object detection methods that associate low level of features obtained. However the proposed approach, low level of HOG features are associated by using Real AdaBoost to continuously achieve features. In this wise, it is possible to capture a shape of edge continuity, which single HOG features can't do, so highly accuracy detection is realized. This paper, to evaluate the effectiveness of the proposed method, three different experiments with different patterns are conducted for detecting humans. Moreover, a boosting classifier is used to represent the co-occurrence of HOG features appearance for detecting a human.

AB - This paper proposes an object detection method that uses Histograms of Oriented Gradients (HOG) features using boosting algorithm. There has been done many research works in late years on statistical learning methods and object detection methods that associate low level of features obtained. However the proposed approach, low level of HOG features are associated by using Real AdaBoost to continuously achieve features. In this wise, it is possible to capture a shape of edge continuity, which single HOG features can't do, so highly accuracy detection is realized. This paper, to evaluate the effectiveness of the proposed method, three different experiments with different patterns are conducted for detecting humans. Moreover, a boosting classifier is used to represent the co-occurrence of HOG features appearance for detecting a human.

KW - Histograms of Oriented Gradients

KW - Real Adaboost

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U2 - 10.1109/ASCC.2015.7244780

DO - 10.1109/ASCC.2015.7244780

M3 - Conference contribution

AN - SCOPUS:84957655871

SN - 9781479978625

BT - 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th Asian Control Conference, ASCC 2015

Y2 - 31 May 2015 through 3 June 2015

ER -

Human tracking method based on improved HOG+Real AdaBoost

Abstract

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Keywords

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