Authors:
Daisuke Sugimura
;
Yusuke Yasukawa
and
Takayuki Hamamoto
Affiliation:
Tokyo University of Science, Japan
Keyword(s):
Hand Gesture Recognition, Low-light Scenes, Motion Blur, Temporal Integration.
Abstract:
We propose a method for recognizing hand gestures in low-light scenes. In such scenes, hand gesture images
are significantly deteriorated because of heavy noise; therefore, previous methods may not work well. In
this study, we exploit a single color image constructed by temporally integrating a hand gesture sequence. In
general, the temporal integration of images improves the signal-to-noise (S/N) ratio; it enables us to capture
sufficient appearance information of the hand gesture sequence. The key idea of this study is to exploit a
motion blur, which is produced when integrating a hand gesture sequence temporally. The direction and the
magnitude of motion blur are discriminative characteristics that can be used for differentiating hand gestures.
In order to extract these features of motion blur, we analyze the gradient intensity and the color distributions
of a single motion-blurred image. In particular, we encode such image features to self-similarity maps, which
capture pairwise
statistics of spatially localized features within a single image. The use of self-similarity maps
allows us to represent invariant characteristics to the individual variations in the same hand gestures. Using
self-similarity maps, we construct a classifier for hand gesture recognition. Our experiments demonstrate the
effectiveness of the proposed method.
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