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Authors: Slim Hamdi 1 ; 2 ; Samir Bouindour 2 ; Kais Loukil 1 ; Hichem Snoussi 2 and Mohamed Abid 1

Affiliations: 1 CES Laboratory, ENIS National Engineering School, University of Sfax, B.P. 3038 Sfax, Tunisia ; 2 LM2S University of Technology of Troyes, 12, rue Marie Curie - CS 42060, 10004 Troyes cedex, France

Keyword(s): Deep Learning, Anomaly Detection, Convolutional Auto-encoder.

Abstract: In the context of abnormal event detection in videos, only the normal events are available for the learning process, therefore the implementation of unsupervised learning method becomes paramount. We propose to use a new architecture denoted Two-Stream Fully Convolutional Networks (TS-FCNs) to extract robust representations able to describe the shapes and movements that can occur in a monitored scene. The learned FCNs are obtained by training two Convolutional Auto-Encoders (CAEs) and extracting the encoder part of each of them. The first CAE is trained with sequences of consecutive frames to extract spatio-temporal features. The second is learned to reconstruct optical flow images from the original images, which provides a better description of the movement. We enhance our (TS-FCN) with a Gaussian classifier in order to detect abnormal spatio-temporal events that could present a security risk. Experimental results on challenging dataset USCD Ped2 shows the effectiveness of the propo sed method compared to the state-of-the-art in abnormal events detection. (More)

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Paper citation in several formats:
Hamdi, S.; Bouindour, S.; Loukil, K.; Snoussi, H. and Abid, M. (2020). Two-streams Fully Convolutional Networks for Abnormal Event Detection in Videos. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-395-7; ISSN 2184-433X, SciTePress, pages 514-521. DOI: 10.5220/0008949405140521

@conference{icaart20,
author={Slim Hamdi. and Samir Bouindour. and Kais Loukil. and Hichem Snoussi. and Mohamed Abid.},
title={Two-streams Fully Convolutional Networks for Abnormal Event Detection in Videos},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2020},
pages={514-521},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008949405140521},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Two-streams Fully Convolutional Networks for Abnormal Event Detection in Videos
SN - 978-989-758-395-7
IS - 2184-433X
AU - Hamdi, S.
AU - Bouindour, S.
AU - Loukil, K.
AU - Snoussi, H.
AU - Abid, M.
PY - 2020
SP - 514
EP - 521
DO - 10.5220/0008949405140521
PB - SciTePress

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