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Detection of anomalies in surveillance scenarios using mixture models
Citation
Tomé Alonso, Adrián and Salgado Álvarez de Sotomayor, Luis (2017). Detection of anomalies in surveillance scenarios using mixture models. In: "International Carnahan Conference on Security Technology (ICCST), 2017", 23/10/2017 - 26/10/2017, Madrid, España. ISBN 978-1-5386-1585-0. pp. 1-4. https://doi.org/10.1109/CCST.2017.8167830.
Description
| Title: | Detection of anomalies in surveillance scenarios using mixture models |
|---|---|
| Author/s: |
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| Item Type: | Presentation at Congress or Conference (Article) |
| Event Title: | International Carnahan Conference on Security Technology (ICCST), 2017 |
| Event Dates: | 23/10/2017 - 26/10/2017 |
| Event Location: | Madrid, España |
| Title of Book: | Proceedings of International Carnahan Conference on Security Technology (ICCST), 2017 |
| Título de Revista/Publicación: | 2017 INTERNATIONAL CARNAHAN CONFERENCE ON SECURITY TECHNOLOGY (ICCST) |
| Date: | December 2017 |
| ISBN: | 978-1-5386-1585-0 |
| Subjects: | |
| Freetext Keywords: | Anomaly detection, Gaussian mixture model, robust optical flow, video-surveillance |
| Faculty: | E.T.S.I. Telecomunicación (UPM) |
| Department: | Señales, Sistemas y Radiocomunicaciones |
| Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
Abstract
In this paper we present a robust and simple method for the detection of anomalies in surveillance scenarios. We use a “bottom-up” approach that avoids any object tracking, making the system suitable for anomaly detection in crowds. A robust optical flow method is used for the extraction of accurate spatio-temporal motion information, which allows to get simple but discriminative descriptors that are employed to train a Gaussian mixture model. We evaluate our system in a publicly available dataset, concluding that our method outperforms similar anomaly detection approaches but with a simpler model and low-sized descriptors.
Funding Projects
| Type | Code | Acronym | Leader | Title |
|---|---|---|---|---|
| Government of Spain | project RTC-2015-3527-1 | BEGISE | Unspecified | Unspecified |
More information
| Item ID: | 50878 |
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| DC Identifier: | http://oa.upm.es/50878/ |
| OAI Identifier: | oai:oa.upm.es:50878 |
| DOI: | 10.1109/CCST.2017.8167830 |
| Official URL: | https://ieeexplore.ieee.org/document/8167830/ |
| Deposited by: | Memoria Investigacion |
| Deposited on: | 29 May 2018 16:52 |
| Last Modified: | 29 May 2018 16:52 |
