Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes
•This paper is one of the first where fully convolutional neural network is used for anomaly detection.•Adapting a pre-trained classification CNN to an FCN for generating video regions to describe motion and shape concurrently.•Proposing a new FCN architecture for time-efficient anomaly detection an...
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| Vydané v: | Computer vision and image understanding Ročník 172; s. 88 - 97 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Inc
01.07.2018
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| ISSN: | 1077-3142, 1090-235X |
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| Abstract | •This paper is one of the first where fully convolutional neural network is used for anomaly detection.•Adapting a pre-trained classification CNN to an FCN for generating video regions to describe motion and shape concurrently.•Proposing a new FCN architecture for time-efficient anomaly detection and localization.•The proposed method performs as well as state-of-the-art methods, but our method outperforms those with respect to time; we ensure real-time for typical applications.
The detection of abnormal behaviour in crowded scenes has to deal with many challenges. This paper presents an efficient method for detection and localization of anomalies in videos. Using fully convolutional neural networks (FCNs) and temporal data, a pre-trained supervised FCN is transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes. High performance in terms of speed and accuracy is achieved by investigating the cascaded detection as a result of reducing computation complexities. This FCN-based architecture addresses two main tasks, feature representation and cascaded outlier detection. Experimental results on two benchmarks suggest that the proposed method outperforms existing methods in terms of accuracy regarding detection and localization. |
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| AbstractList | •This paper is one of the first where fully convolutional neural network is used for anomaly detection.•Adapting a pre-trained classification CNN to an FCN for generating video regions to describe motion and shape concurrently.•Proposing a new FCN architecture for time-efficient anomaly detection and localization.•The proposed method performs as well as state-of-the-art methods, but our method outperforms those with respect to time; we ensure real-time for typical applications.
The detection of abnormal behaviour in crowded scenes has to deal with many challenges. This paper presents an efficient method for detection and localization of anomalies in videos. Using fully convolutional neural networks (FCNs) and temporal data, a pre-trained supervised FCN is transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes. High performance in terms of speed and accuracy is achieved by investigating the cascaded detection as a result of reducing computation complexities. This FCN-based architecture addresses two main tasks, feature representation and cascaded outlier detection. Experimental results on two benchmarks suggest that the proposed method outperforms existing methods in terms of accuracy regarding detection and localization. |
| Author | Moayed, Zahra Sabokrou, Mohammad Klette, Reinhard Fayyaz, Mohsen Fathy, Mahmood |
| Author_xml | – sequence: 1 givenname: Mohammad orcidid: 0000-0002-9409-2799 surname: Sabokrou fullname: Sabokrou, Mohammad email: sabokro@ipm.ir organization: School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran PO Box 19395-5746, Iran – sequence: 2 givenname: Mohsen surname: Fayyaz fullname: Fayyaz, Mohsen email: fayyaz@iai.uni-bonn.de organization: University of Bonn, Germany – sequence: 3 givenname: Mahmood surname: Fathy fullname: Fathy, Mahmood email: mfathy@ipm.ir organization: School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran PO Box 19395-5746, Iran – sequence: 4 givenname: Zahra surname: Moayed fullname: Moayed, Zahra email: zmoayed@aut.ac.nz organization: School of Engineering, Computer and Mathematical Sciences, EEE Department, Auckland University of Technology, Auckland, New Zealand – sequence: 5 givenname: Reinhard surname: Klette fullname: Klette, Reinhard email: rklette@aut.ac.nz organization: School of Engineering, Computer and Mathematical Sciences, EEE Department, Auckland University of Technology, Auckland, New Zealand |
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| SubjectTerms | CNN Real-time processing Transfer learning Video anomaly detection |
| Title | Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes |
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