Low Latency Action Recognition with Depth Information

Ali Seydi Keceli, Ahmet Burak Can

2016

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.

Paper Citation

in Harvard Style

Keceli A. and Can A. (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 - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 590-596. DOI: 10.5220/0005723005900596

in Bibtex Style

@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 - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={590-596},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005723005900596},
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 - Low Latency Action Recognition with Depth Information
SN - 978-989-758-175-5
AU - Keceli A.
AU - Can A.
PY - 2016
SP - 590
EP - 596
DO - 10.5220/0005723005900596