Author
Kıraç, Mustafa Furkan, Kara, Y. E., Akarun, L.
Publication Date
2014-12-01
Publication Place
-
Elsevier
Subject
Hand gesture, Articulated hand pose, Depth image, Kinect, Decision tree
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1872-7344
Record ID
7d17b8d0-d82c-4366-871a-9bb8ac06912e
Library Location
Computer Science
Date
2014-12-01
Notes
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text
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
