Hareketsiz Görüntülerdeki Yayalarin Çoklu Siniflandiricilar ile Tespiti

Caglar Cavdar; Abdullah Ozek; Bulent Bolat

doi:10.1109/SIU.2018.8404288

Hareketsiz Görüntülerdeki Yayalarin Çoklu Siniflandiricilar ile Tespiti

Translated title of the contribution: Pedestrian detection with multiple classifiers on still images

Caglar Cavdar, Abdullah Ozek, Bulent Bolat

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

Abstract

In this work, an algorithm that detects pedestrians in still images using different classifiers is presented. HOG, which is frequently used in pedestrian detection, and support vector machine (SVM), K nearest neighbors (KNN) and AdaBoost algorithms were used as descriptors. It is decided whether the image is pedestrian by looking at the result of three different classifiers. In order to demonstrate the effectiveness of the method, the system is trained using the INRIA data set and tested by using Penn Fudan Pedestrian Dataset which is different dataset. Experimental results show that the proposed method detects higher accuracy than pedestrian detection using a single classifier.

Translated title of the contribution	Pedestrian detection with multiple classifiers on still images
Original language	Turkish
Title of host publication	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781538615010
DOIs	https://doi.org/10.1109/SIU.2018.8404288
Publication status	Published - 5 Jul 2018
Externally published	Yes
Event	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Duration: 2 May 2018 → 5 May 2018

Publication series

Name	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Conference

Conference	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Country/Territory	Turkey
City	Izmir
Period	2/05/18 → 5/05/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Access to Document

10.1109/SIU.2018.8404288

Cite this

Cavdar, C., Ozek, A., & Bolat, B. (2018). Hareketsiz Görüntülerdeki Yayalarin Çoklu Siniflandiricilar ile Tespiti. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1-4). (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2018.8404288

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title = "Hareketsiz G{\"o}r{\"u}nt{\"u}lerdeki Yayalarin {\c C}oklu Siniflandiricilar ile Tespiti",

abstract = "In this work, an algorithm that detects pedestrians in still images using different classifiers is presented. HOG, which is frequently used in pedestrian detection, and support vector machine (SVM), K nearest neighbors (KNN) and AdaBoost algorithms were used as descriptors. It is decided whether the image is pedestrian by looking at the result of three different classifiers. In order to demonstrate the effectiveness of the method, the system is trained using the INRIA data set and tested by using Penn Fudan Pedestrian Dataset which is different dataset. Experimental results show that the proposed method detects higher accuracy than pedestrian detection using a single classifier.",

keywords = "AdaBoost, HOG, K-nearest neighbors algorithm, Pedestiran detection, Support vector machine",

author = "Caglar Cavdar and Abdullah Ozek and Bulent Bolat",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 ; Conference date: 02-05-2018 Through 05-05-2018",

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doi = "10.1109/SIU.2018.8404288",

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

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Cavdar, C, Ozek, A & Bolat, B 2018, Hareketsiz Görüntülerdeki Yayalarin Çoklu Siniflandiricilar ile Tespiti. in 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Turkey, 2/05/18. https://doi.org/10.1109/SIU.2018.8404288

Hareketsiz Görüntülerdeki Yayalarin Çoklu Siniflandiricilar ile Tespiti. / Cavdar, Caglar; Ozek, Abdullah; Bolat, Bulent.
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).

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

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T1 - Hareketsiz Görüntülerdeki Yayalarin Çoklu Siniflandiricilar ile Tespiti

AU - Cavdar, Caglar

AU - Ozek, Abdullah

AU - Bolat, Bulent

PY - 2018/7/5

Y1 - 2018/7/5

N2 - In this work, an algorithm that detects pedestrians in still images using different classifiers is presented. HOG, which is frequently used in pedestrian detection, and support vector machine (SVM), K nearest neighbors (KNN) and AdaBoost algorithms were used as descriptors. It is decided whether the image is pedestrian by looking at the result of three different classifiers. In order to demonstrate the effectiveness of the method, the system is trained using the INRIA data set and tested by using Penn Fudan Pedestrian Dataset which is different dataset. Experimental results show that the proposed method detects higher accuracy than pedestrian detection using a single classifier.

AB - In this work, an algorithm that detects pedestrians in still images using different classifiers is presented. HOG, which is frequently used in pedestrian detection, and support vector machine (SVM), K nearest neighbors (KNN) and AdaBoost algorithms were used as descriptors. It is decided whether the image is pedestrian by looking at the result of three different classifiers. In order to demonstrate the effectiveness of the method, the system is trained using the INRIA data set and tested by using Penn Fudan Pedestrian Dataset which is different dataset. Experimental results show that the proposed method detects higher accuracy than pedestrian detection using a single classifier.

KW - AdaBoost

KW - HOG

KW - K-nearest neighbors algorithm

KW - Pedestiran detection

KW - Support vector machine

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DO - 10.1109/SIU.2018.8404288

M3 - Konferans katkısı

AN - SCOPUS:85050820638

T3 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

SP - 1

EP - 4

BT - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Y2 - 2 May 2018 through 5 May 2018

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Cavdar C, Ozek A, Bolat B. Hareketsiz Görüntülerdeki Yayalarin Çoklu Siniflandiricilar ile Tespiti. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-4. (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). doi: 10.1109/SIU.2018.8404288

Hareketsiz Görüntülerdeki Yayalarin Çoklu Siniflandiricilar ile Tespiti

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