Hand posture recognition and tracking based on Bag-of-Words for human robot interaction

Hand posture is a natural and effective interaction between human and robot. In this paper, we use monocular camera as input device, and an improved Bag-of-Words (BoW) method is proposed to detect and recognize hand posture based on a new descriptor ARPD (Appearance and Relative Position Descriptor)...

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Bibliographic Details
Published in2011 IEEE International Conference on Robotics and Automation pp. 538 - 543
Main Authors Yuelong Chuang, Ling Chen, Gangqiang Zhao, Gencai Chen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2011
Subjects
Algorithm design and analysis
Classification algorithms
Clustering algorithms
Histograms
Target tracking
Training
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Summary:Hand posture is a natural and effective interaction between human and robot. In this paper, we use monocular camera as input device, and an improved Bag-of-Words (BoW) method is proposed to detect and recognize hand posture based on a new descriptor ARPD (Appearance and Relative Position Descriptor) and spectral embedding clustering algorithm. To track hand motion rapidly and accurately, we have designed a new framework based on improved BoW and CAMSHIFT algorithm. The thorough evaluation of our algorithm is presented to show its usefulness.
ISBN:9781612843865
1612843867
ISSN:1050-4729
2577-087X
DOI:10.1109/ICRA.2011.5979767