DocumentCode :
588914
Title :
Hands Detection Based on Statistical Learning
Author :
Hui Li ; Lei Yang ; Xiaoyu Wu ; Jun Zhai
Author_Institution :
Digital Media Dept., Commun. Univ. of China, Beijing, China
Volume :
2
fYear :
2012
fDate :
28-29 Oct. 2012
Firstpage :
227
Lastpage :
230
Abstract :
This paper proposes a hand detection methodbased on statistical learning training way. Using Microsoft´s Kinect sensor, to get the depth information. Through the analysis of the characetristics of hands, put out a kind of new features for statistical learning which approximate with Harr-like feature. The new feature is good at describing complex hand shape degeneration. With the help of Adaboost statistical learning, gets the training model. Experiment results demonstrate that using the new features with Adaboost algorithm can achieve more rapid and robust hands detection system.
Keywords :
approximation theory; feature extraction; image sensors; object detection; statistical analysis; Adaboost statistical learning; Harr-like feature extraction; Microsoft Kinect sensor; approximation; depth information; hand shape degeneration; hands detection system; Educational institutions; Feature extraction; Object detection; Real-time systems; Shape; Statistical learning; Training; Adaboost; Harr-like; Kinect; hands detection; training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-2646-9
Type :
conf
DOI :
10.1109/ISCID.2012.208
Filename :
6405971
Link To Document :
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