Hierarchically constrained 3D hand pose estimation using regression forests from single frame depth data

Name: Hierarchically constrained 3D hand pose estimation using regression forests from single frame depth data
Author: Kıraç, Mustafa Furkan, Kara, Y. E., Akarun, L.

İsim	Hierarchically constrained 3D hand pose estimation using regression forests from single frame depth data
Yazar	Kıraç, Mustafa Furkan, Kara, Y. E., Akarun, L.
Basım Tarihi:	2014-12-01
Basım Yeri	- Elsevier
Konu	Hand gesture, Articulated hand pose, Depth image, Kinect, Decision tree
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1872-7344
Kayıt Numarası	7d17b8d0-d82c-4366-871a-9bb8ac06912e
Lokasyon	Computer Science
Tarih	2014-12-01
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	The emergence of inexpensive 2.5D depth cameras has enabled the extraction of the articulated human body pose. However, human hand skeleton extraction still stays as a challenging problem since the hand contains as many joints as the human body model. The small size of the hand also makes the problem more challenging due to resolution limits of the depth cameras. Moreover, hand poses suffer from self-occlusion which is considerably less likely in a body pose. This paper describes a scheme for extracting the hand skeleton using random regression forests in real-time that is robust to self- occlusion and low resolution of the depth camera. In addition to that, the proposed algorithm can estimate the joint positions even if all of the pixels related to a joint are out of the camera frame. The performance of the new method is compared to the random classification forests based method in the literature. Moreover, the performance of the joint estimation is further improved using a novel hierarchical mode selection algorithm that makes use of constraints imposed by the skeleton geometry. The performance of the proposed algorithm is tested on datasets containing synthetic and real data, where self-occlusion is frequently encountered. The new algorithm which runs in real time using a single depth image is shown to outperform previous methods.
DOI	10.1016/j.patrec.2013.09.003
Cilt	50

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Hierarchically constrained 3D hand pose estimation using regression forests from single frame depth data

Yazar Kıraç, Mustafa Furkan, Kara, Y. E., Akarun, L.

Basım Tarihi 2014-12-01

Basım Yeri - Elsevier

Konu Hand gesture, Articulated hand pose, Depth image, Kinect, Decision tree

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1872-7344

Kayıt Numarası 7d17b8d0-d82c-4366-871a-9bb8ac06912e

Lokasyon Computer Science

Tarih 2014-12-01

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin The emergence of inexpensive 2.5D depth cameras has enabled the extraction of the articulated human body pose. However, human hand skeleton extraction still stays as a challenging problem since the hand contains as many joints as the human body model. The small size of the hand also makes the problem more challenging due to resolution limits of the depth cameras. Moreover, hand poses suffer from self-occlusion which is considerably less likely in a body pose. This paper describes a scheme for extracting the hand skeleton using random regression forests in real-time that is robust to self- occlusion and low resolution of the depth camera. In addition to that, the proposed algorithm can estimate the joint positions even if all of the pixels related to a joint are out of the camera frame. The performance of the new method is compared to the random classification forests based method in the literature. Moreover, the performance of the joint estimation is further improved using a novel hierarchical mode selection algorithm that makes use of constraints imposed by the skeleton geometry. The performance of the proposed algorithm is tested on datasets containing synthetic and real data, where self-occlusion is frequently encountered. The new algorithm which runs in real time using a single depth image is shown to outperform previous methods.

DOI 10.1016/j.patrec.2013.09.003

Cilt 50