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Authors: Tuan-Hung Vu 1 ; Sebastien Ambellouis 2 ; Jacques Boonaert 1 and Abdelmalik Taleb-Ahmed 3

Affiliations: 1 Departement Informatique & Automatique, IMT Lille Douai, France ; 2 COSYS, IFSTTAR, France ; 3 IEMN DOAE UMR CNRS 8520, Université Polytechnique Hauts-de-France, France

Keyword(s): Anomaly Detection, Future Prediction, Deep Learning, Appearance and Motion Features.

Abstract: Anomaly detection in surveillance videos is the identification of rare events which produce different features from normal events. In this paper, we present a survey about the progress of anomaly detection techniques and introduce our proposed framework to tackle this very challenging objective. Our approach is based on the more recent state-of-the-art techniques and casts anomalous events as unexpected events in future frames. Our framework is so flexible that you can replace almost important modules by existing state-of-the-art methods. The most popular solutions only use future predicted informations as constraints for training a convolutional encode-decode network to reconstruct frames and take the score of the difference between both original and reconstructed information. We propose a fully future prediction based framework that directly defines the feature as the difference between both future predictions and ground truth informations. This feature can be fed into various type s of learning model to assign anomaly label. We present our experimental plan and argue that our framework’s performance will be competitive with state-of-the art scores by presenting early promising results in feature extraction. (More)

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Paper citation in several formats:
Vu, T.; Ambellouis, S.; Boonaert, J. and Taleb-Ahmed, A. (2020). Anomaly Detection in Surveillance Videos by Future Appearance-motion Prediction. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 484-490. DOI: 10.5220/0009146704840490

@conference{visapp20,
author={Tuan{-}Hung Vu. and Sebastien Ambellouis. and Jacques Boonaert. and Abdelmalik Taleb{-}Ahmed.},
title={Anomaly Detection in Surveillance Videos by Future Appearance-motion Prediction},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={484-490},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009146704840490},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - Anomaly Detection in Surveillance Videos by Future Appearance-motion Prediction
SN - 978-989-758-402-2
IS - 2184-4321
AU - Vu, T.
AU - Ambellouis, S.
AU - Boonaert, J.
AU - Taleb-Ahmed, A.
PY - 2020
SP - 484
EP - 490
DO - 10.5220/0009146704840490
PB - SciTePress

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