Journal of imaging, 2018-02, Vol.4 (2), p.36
2018
Details
- Autor(en) / Beteiligte
- Titel
- An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos
- Ist Teil von
- Journal of imaging, 2018-02, Vol.4 (2), p.36
- Ort / Verlag
- Basel: MDPI AG
- Erscheinungsjahr
- 2018
- Link zum Volltext
- Quelle
- EZB Electronic Journals Library
- Beschreibungen/Notizen
- Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 2313-433XeISSN: 2313-433XDOI: 10.3390/jimaging4020036Titel-ID: cdi_doaj_primary_oai_doaj_org_article_216bd7d740984d85aac0c39b2bc37f2f
- Format
- –
- Schlagworte
- Annotations, Anomalies, anomaly detection, Artificial intelligence, autoencoders, Cases (containers), Data mining, Datasets, Deep learning, generative adversarial networks, International conferences, LSTMs, Neural networks, Pattern recognition, Pedestrians, predictive models, representation learning, State-of-the-art reviews, unsupervised methods, Variational Autoencoders