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Authors: Ali Seydi Keceli and Ahmet Burak Can

Affiliation: Hacettepe University, Turkey

Keyword(s): Action Recognition, RGB-D Sensor, Adaboost, SVM, Low Latency.

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

Abstract: In this study an approach for low latency action recognition is proposed. Low latency action recognition aims to recognize actions without observing the whole action sequence. In the proposed approach, a skeletal model is obtained from depth images. Features extracted from the skeletal model are considered as time series and histograms. To classify actions, Adaboost M1 classifier is utilized with an SVM kernel. The trained classifiers are tested with different action observation ratios and compared with some of the studies in the literature. The model produces promising results without observing the whole action sequence.

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Paper citation in several formats:
Keceli, A. S. and Can, A. B. (2016). Low Latency Action Recognition with Depth Information. In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP; ISBN 978-989-758-175-5; ISSN 2184-4321, SciTePress, pages 590-596. DOI: 10.5220/0005723005900596

@conference{visapp16,
author={Ali Seydi Keceli and Ahmet Burak Can},
title={Low Latency Action Recognition with Depth Information},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP},
year={2016},
pages={590-596},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005723005900596},
isbn={978-989-758-175-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP
TI - Low Latency Action Recognition with Depth Information
SN - 978-989-758-175-5
IS - 2184-4321
AU - Keceli, A.
AU - Can, A.
PY - 2016
SP - 590
EP - 596
DO - 10.5220/0005723005900596
PB - SciTePress