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Authors: Adrien Chan-Hon-Tong 1 ; Nicolas Ballas 1 ; Bertrand Delezoide 1 ; Catherine Achard 2 ; Laurent Lucat 1 ; Patrick Sayd 1 and Françoise Prêteux 3

Affiliations: 1 CEA, LIST and DIASI, France ; 2 UPMC, France ; 3 Mines ParisTech, France

ISBN: 978-989-8565-47-1

ISSN: 2184-4321

Keyword(s): Skeleton Trajectory, Human Activity Classification.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis

Abstract: Automatic human action annotation is a challenging problem, which overlaps with many computer vision fields such as video-surveillance, human-computer interaction or video mining. In this work, we offer a skeleton based algorithm to classify segmented human-action sequences. Our contribution is twofold. First, we offer and evaluate different trajectory descriptors on skeleton datasets. Six short term trajectory features based on position, speed or acceleration are first introduced. The last descriptor is the most original since it extends the well-known bag-of-words approach to the bag-of-gestures ones for 3D position of articulations. All these descriptors are evaluated on two public databases with state-of-the art machine learning algorithms. The second contribution is to measure the influence of missing data on algorithms based on skeleton. Indeed skeleton extraction algorithms commonly fail on real sequences, with side or back views and very complex postures. Thus on these real da ta, we offer to compare recognition methods based on image and those based on skeleton with many missing data. (More)

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Paper citation in several formats:
Chan-Hon-Tong, A.; Ballas, N.; Achard, C.; Delezoide, B.; Lucat, L.; Sayd, P. and Prêteux, F. (2013). Skeleton Point Trajectories for Human Daily Activity Recognition.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, ISSN 2184-4321, pages 520-529. DOI: 10.5220/0004202805200529

@conference{visapp13,
author={Adrien Chan{-}Hon{-}Tong. and Nicolas Ballas. and Catherine Achard. and Bertrand Delezoide. and Laurent Lucat. and Patrick Sayd. and Fran\c{C}oise Prêteux.},
title={Skeleton Point Trajectories for Human Daily Activity Recognition},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={520-529},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004202805200529},
isbn={978-989-8565-47-1},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Skeleton Point Trajectories for Human Daily Activity Recognition
SN - 978-989-8565-47-1
AU - Chan-Hon-Tong, A.
AU - Ballas, N.
AU - Achard, C.
AU - Delezoide, B.
AU - Lucat, L.
AU - Sayd, P.
AU - Prêteux, F.
PY - 2013
SP - 520
EP - 529
DO - 10.5220/0004202805200529

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