Anomaly Detection in Surveillance Videos by Future Appearance-motion Prediction

Vu, Tuan-Hung; Ambellouis, Sebastien; Boonaert, Jacques; Tahleb Ahmed, Abdelmalik

Document type :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes

DOI :

10.5220/0009146704840490

Title :

Anomaly Detection in Surveillance Videos by Future Appearance-motion Prediction

Author(s) :

Vu, Tuan-Hung [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]
Ambellouis, Sebastien [Auteur]
Laboratoire Électronique Ondes et Signaux pour les Transports [COSYS-LEOST ]
Boonaert, Jacques [Auteur]
Centre for Digital Systems [CERI SN - IMT Nord Europe]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]
Tahleb Ahmed, Abdelmalik [Auteur]
Université de Valenciennes et du Hainaut-Cambrésis [UVHC]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]

Conference title :

15th International Conference on Computer Vision Theory and Applications

City :

Valletta

Country :

France

Start date of the conference :

2020-02-27

Publisher :

SCITEPRESS - Science and Technology Publications

Publication date :

2020

English keyword(s) :

Anomaly Detection
Future Prediction
Deep Learning
Appearance and Motion Features

HAL domain(s) :

Informatique [cs]

English abstract : [en]

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 ...
Show more >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 information 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 information. This feature can be fed into various types 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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL

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