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Authors: Ken Yano ; Takeshi Ogawa ; Motoaki Kawanabe and Takayuki Suyama

Affiliation: Advanced Telecommunications Research Institute International, Japan

ISBN: 978-989-758-090-1

Keyword(s): Behavior Analysis, Gesture Recognition, Randomized Clustering Forests, Human Machine Interface.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Enterprise Information Systems ; Features Extraction ; Human and Computer Interaction ; Human-Computer Interaction ; Image and Video Analysis ; Image Formation and Preprocessing ; Image Generation Pipeline: Algorithms and Techniques ; Motion, Tracking and Stereo Vision ; Optical Flow and Motion Analyses

Abstract: Behavior recognition has been one of the hot topics in the field of computer vision and its application. The popular appearance-based behavior classification methods often utilize sparse spatio-temporal features that capture the salient features and then use a visual word dictionary to construct visual words. Visual word assignments based on K-means clustering are very effective and behave well for general behavior classification. However, these pipelines often demand high computational power for the stages for low visual feature extraction and visual word assignment, and thus they are not suitable for real-time recognition tasks. To overcome the inefficient processing of K-means and the nearest neighbor approach, an ensemble approach is used for fast processing. For real-time recognition, an ensemble of random trees seems particularly suitable for visual dictionaries owing to its simplicity, speed, and performance. In this paper, we focus on the real-time recognition by utilizing a r andom clustering forest and verifying its effectiveness by classifying various hand gestures. In addition, we proposed a boosted random clustering forest so that training time can be successfully shortened with minimal negative impact on its recognition rate. For an application, we demonstrated a possible use of real-time gesture recognition by controlling a digital TV using hand gestures. (More)

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Paper citation in several formats:
Yano, K.; Ogawa, T.; Kawanabe, M. and Suyama, T. (2015). On-line Hand Gesture Recognition to Control Digital TV using a Boosted and Randomized Clustering Forest.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 220-227. DOI: 10.5220/0005263502200227

@conference{visapp15,
author={Ken Yano. and Takeshi Ogawa. and Motoaki Kawanabe. and Takayuki Suyama.},
title={On-line Hand Gesture Recognition to Control Digital TV using a Boosted and Randomized Clustering Forest},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={220-227},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005263502200227},
isbn={978-989-758-090-1},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - On-line Hand Gesture Recognition to Control Digital TV using a Boosted and Randomized Clustering Forest
SN - 978-989-758-090-1
AU - Yano, K.
AU - Ogawa, T.
AU - Kawanabe, M.
AU - Suyama, T.
PY - 2015
SP - 220
EP - 227
DO - 10.5220/0005263502200227

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