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

Caglar Cavdar
, Abdullah Ozek
, Bulent Bolat

NETAŞ Telekomunikasyon A.Ş
Yildiz Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

1 Atıf (Scopus)

Özet

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.

Tercüme edilen katkı başlığı	Pedestrian detection with multiple classifiers on still images
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	1-4
Sayfa sayısı	4
ISBN (Elektronik)	9781538615010
DOI'lar	https://doi.org/10.1109/SIU.2018.8404288
Yayın durumu	Yayınlandı - 5 Tem 2018
Harici olarak yayınlandı	Evet
Etkinlik	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Türkiye Süre: 2 May 2018 → 5 May 2018

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Ülke/Bölge	Türkiye
Şehir	Izmir
Periyot	2/05/18 → 5/05/18

Bibliyografik not

Publisher Copyright:
© 2018 IEEE.

Keywords

AdaBoost
HOG
K-nearest neighbors algorithm
Pedestiran detection
Support vector machine

Belgeye Erişim

10.1109/SIU.2018.8404288

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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

@inproceedings{96e7c18de6384360be0660cc9ec11f52,

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",

year = "2018",

month = jul,

day = "5",

doi = "10.1109/SIU.2018.8404288",

language = "T{\"u}rk{\c c}e",

series = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",

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

pages = "1--4",

booktitle = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",

address = "United States",

}

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, Türkiye, 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).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

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

UR - https://www.scopus.com/pages/publications/85050820638

U2 - 10.1109/SIU.2018.8404288

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

ER -

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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap