RGB-D Görüntülerinden Insan Aktivitesi Tanima ve Siniflandirma

Deniz Gürkaynak; Hülya Yalçin

doi:10.1109/SIU.2015.7130190

RGB-D Görüntülerinden Insan Aktivitesi Tanima ve Siniflandirma

Deniz Gürkaynak, Hülya Yalçin

Makina Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

3 Atıf (Scopus)

Özet

Human activity recognition has many applications in computer vision, including personal assistive robotics and smart homes/environments. Due to the large temporal and spatial variations in actions performed by humans, human action recognition has been a long-standing challenge. This paper presents a method that recognizes certain human activities based on a motion descriptor that uses 3D human skeleton data. A motion descriptor (SHOJD) is defined using the 3D distance between the most frequent key poses that occur throughout the action that is intended to be recognized. SHOJD features are then fed into an artificial neural network for classification. Experimental results indicate that the SHOJD based human action recognition system is robust with high recognition rate.

Tercüme edilen katkı başlığı	Recognition and classification of human activity from RGB-D videos
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	1745-1748
Sayfa sayısı	4
ISBN (Elektronik)	9781467373869
DOI'lar	https://doi.org/10.1109/SIU.2015.7130190
Yayın durumu	Yayınlandı - 19 Haz 2015
Etkinlik	2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Malatya, Turkey Süre: 16 May 2015 → 19 May 2015

Yayın serisi

Adı	2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings

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

???event.eventtypes.event.conference???	2015 23rd Signal Processing and Communications Applications Conference, SIU 2015
Ülke/Bölge	Turkey
Şehir	Malatya
Periyot	16/05/15 → 19/05/15

Bibliyografik not

Publisher Copyright:
© 2015 IEEE.

Keywords

RGB-D imaging
activity recognition
motion descriptors

Belgeye Erişim

10.1109/SIU.2015.7130190

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Gürkaynak, D., & Yalçin, H. (2015). RGB-D Görüntülerinden Insan Aktivitesi Tanima ve Siniflandirma. In 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings (pp. 1745-1748). Makale 7130190 (2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2015.7130190

Gürkaynak, Deniz ; Yalçin, Hülya. / RGB-D Görüntülerinden Insan Aktivitesi Tanima ve Siniflandirma. 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 1745-1748 (2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings).

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abstract = "Human activity recognition has many applications in computer vision, including personal assistive robotics and smart homes/environments. Due to the large temporal and spatial variations in actions performed by humans, human action recognition has been a long-standing challenge. This paper presents a method that recognizes certain human activities based on a motion descriptor that uses 3D human skeleton data. A motion descriptor (SHOJD) is defined using the 3D distance between the most frequent key poses that occur throughout the action that is intended to be recognized. SHOJD features are then fed into an artificial neural network for classification. Experimental results indicate that the SHOJD based human action recognition system is robust with high recognition rate.",

keywords = "RGB-D imaging, activity recognition, motion descriptors",

author = "Deniz G{\"u}rkaynak and H{\"u}lya Yal{\c c}in",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 ; Conference date: 16-05-2015 Through 19-05-2015",

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Gürkaynak, D & Yalçin, H 2015, RGB-D Görüntülerinden Insan Aktivitesi Tanima ve Siniflandirma. in 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings., 7130190, 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1745-1748, 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015, Malatya, Turkey, 16/05/15. https://doi.org/10.1109/SIU.2015.7130190

RGB-D Görüntülerinden Insan Aktivitesi Tanima ve Siniflandirma. / Gürkaynak, Deniz; Yalçin, Hülya.
2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1745-1748 7130190 (2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - RGB-D Görüntülerinden Insan Aktivitesi Tanima ve Siniflandirma

AU - Gürkaynak, Deniz

AU - Yalçin, Hülya

PY - 2015/6/19

Y1 - 2015/6/19

N2 - Human activity recognition has many applications in computer vision, including personal assistive robotics and smart homes/environments. Due to the large temporal and spatial variations in actions performed by humans, human action recognition has been a long-standing challenge. This paper presents a method that recognizes certain human activities based on a motion descriptor that uses 3D human skeleton data. A motion descriptor (SHOJD) is defined using the 3D distance between the most frequent key poses that occur throughout the action that is intended to be recognized. SHOJD features are then fed into an artificial neural network for classification. Experimental results indicate that the SHOJD based human action recognition system is robust with high recognition rate.

AB - Human activity recognition has many applications in computer vision, including personal assistive robotics and smart homes/environments. Due to the large temporal and spatial variations in actions performed by humans, human action recognition has been a long-standing challenge. This paper presents a method that recognizes certain human activities based on a motion descriptor that uses 3D human skeleton data. A motion descriptor (SHOJD) is defined using the 3D distance between the most frequent key poses that occur throughout the action that is intended to be recognized. SHOJD features are then fed into an artificial neural network for classification. Experimental results indicate that the SHOJD based human action recognition system is robust with high recognition rate.

KW - RGB-D imaging

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KW - motion descriptors

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Gürkaynak D, Yalçin H. RGB-D Görüntülerinden Insan Aktivitesi Tanima ve Siniflandirma. In 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 1745-1748. 7130190. (2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings). doi: 10.1109/SIU.2015.7130190

RGB-D Görüntülerinden Insan Aktivitesi Tanima ve Siniflandirma

Özet

Yayın serisi

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

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

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