DEPTH DATA-DRIVEN REAL-TIME ARTICULATED HAND POSE RECOGNITION

Title
DEPTH DATA-DRIVEN REAL-TIME ARTICULATED HAND POSE RECOGNITION
Authors
차영운임화섭성민혁안상철
Keywords
depth data; hand pose recognition
Issue Date
2014-12
Publisher
Lecture notes in computer science
Citation
VOL 8888, 492-501
Abstract
This paper presents a fast but robust method to recognize articulated hand pose from single depth images in real-time. We tackle the main challenges in the hand pose recognition, which include the high degree of freedom and self-occlusion of articulated hand motion, using e cient retrieval of a large set of hand pose templates. The normalized orientation templates are used for encoding the depth images containing hand poses, and the locality sensitive hashing is used for nding the nearest neighbors in real time. Our approach does not su er from the common problems in the conventional tracking approaches such as model initialization and tracking drift, and qualitatively outperforms the existing hand pose estimation techniques.
URI
http://pubs.kist.re.kr/handle/201004/49107
ISSN
03029743
Appears in Collections:
KIST Publication > Article
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