Using Motion Blur to Recognize Hand Gestures in Low-light Scenes

Daisuke Sugimura, Yusuke Yasukawa, Takayuki Hamamoto

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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Paper Citation


in Harvard Style

Sugimura D., Yasukawa Y. and Hamamoto T. (2016). Using Motion Blur to Recognize Hand Gestures in Low-light Scenes . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 308-316. DOI: 10.5220/0005673603080316


in Bibtex Style

@conference{visapp16,
author={Daisuke Sugimura and Yusuke Yasukawa and Takayuki Hamamoto},
title={Using Motion Blur to Recognize Hand Gestures in Low-light Scenes},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={308-316},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005673603080316},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Using Motion Blur to Recognize Hand Gestures in Low-light Scenes
SN - 978-989-758-175-5
AU - Sugimura D.
AU - Yasukawa Y.
AU - Hamamoto T.
PY - 2016
SP - 308
EP - 316
DO - 10.5220/0005673603080316