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Proceedings Paper

Real-time affine invariant gesture recognition for LED smart lighting control
Author(s): Xu Chen; Miao Liao; Xiao-Fan Feng
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Paper Abstract

Gesture recognition has attracted extensive research interest in the field of human computer interaction. Realtime affine invariant gesture recognition is an important and challenging problem. This paper presents a robust affine view invariant gesture recognition system for realtime LED smart light control. As far as we know, this is the first time that gesture recognition has been applied for control LED smart light in realtime. Employing skin detection, hand blobs captured from a top view camera are first localized and aligned. Subsequently, SVM classifiers trained on HOG features and robust shape features are then utilized for gesture recognition. By accurately recognizing two types of gestures (“gesture 8" and a “5 finger gesture"), a user is enabled to toggle lighting on/off efficiently and control light intensity on a continuous scale. In each case, gesture recognition is rotation- and translation-invariant. Extensive evaluations in an office setting demonstrate the effectiveness and robustness of the proposed gesture recognition algorithm.

Paper Details

Date Published: 16 March 2015
PDF: 12 pages
Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 939906 (16 March 2015); doi: 10.1117/12.2077329
Show Author Affiliations
Xu Chen, Sharp Labs. of America, Inc. (United States)
Miao Liao, Sharp Labs. of America, Inc. (United States)
Xiao-Fan Feng, Sharp Labs. of America, Inc. (United States)

Published in SPIE Proceedings Vol. 9399:
Image Processing: Algorithms and Systems XIII
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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