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Authors: Hirokatsu Kataoka 1 ; Yoshimitsu Aoki 2 ; Kenji Iwata 1 and Yutaka Satoh 1

Affiliations: 1 National Institute of Advanced Industrial Science and Technology (AIST), Japan ; 2 Keio University, Japan

Keyword(s): Activity Prediction, Space-Time Convolutional Neural Networks (CNN), Bayesian Classifier, Dense Trajectories.

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

Abstract: We present a technique to address the new challenge of activity prediction in computer vision field. In activity prediction, we infer the next human activity through "classified activities" and "activity data analysis.” Moreover, the prediction should be processed in real-time to avoid dangerous or anomalous activities. The combination of space--time convolutional neural networks (ST-CNN) and improved dense trajectories (iDT) are able to effectively understand human activities in image sequences. After categorizing human activities, we insert activity tags into an activity database in order to sample a distribution of human activity. A naive Bayes classifier allows us to achieve real-time activity prediction because only three elements are needed for parameter estimation. The contributions of this paper are: (i) activity prediction within a Bayesian framework and (ii) ST-CNN and iDT features for activity recognition. Moreover, human activity prediction in real-scenes is achieved wi th 81.0% accuracy. (More)

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Paper citation in several formats:
Kataoka, H.; Aoki, Y.; Iwata, K. and Satoh, Y. (2016). Activity Prediction using a Space-Time CNN and Bayesian Framework. 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 461-469. DOI: 10.5220/0005671704610469

@conference{visapp16,
author={Hirokatsu Kataoka. and Yoshimitsu Aoki. and Kenji Iwata. and Yutaka Satoh.},
title={Activity Prediction using a Space-Time CNN and Bayesian Framework},
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={461-469},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005671704610469},
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 - Activity Prediction using a Space-Time CNN and Bayesian Framework
SN - 978-989-758-175-5
IS - 2184-4321
AU - Kataoka, H.
AU - Aoki, Y.
AU - Iwata, K.
AU - Satoh, Y.
PY - 2016
SP - 461
EP - 469
DO - 10.5220/0005671704610469
PB - SciTePress

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