Texto completo
|
PDF (Document Portable Format)
- Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (2MB) | Vista Previa |
Detection of anomalies in surveillance scenarios using mixture models
Cita
Tomé Alonso, Adrián y Salgado Álvarez de Sotomayor, Luis (2017). Detection of anomalies in surveillance scenarios using mixture models. En: "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.
Descripción
| Título: | Detection of anomalies in surveillance scenarios using mixture models |
|---|---|
| Autor/es: |
|
| Tipo de Documento: | Ponencia en Congreso o Jornada (Artículo) |
| Título del Evento: | International Carnahan Conference on Security Technology (ICCST), 2017 |
| Fechas del Evento: | 23/10/2017 - 26/10/2017 |
| Lugar del Evento: | Madrid, España |
| Título del Libro: | Proceedings of International Carnahan Conference on Security Technology (ICCST), 2017 |
| Título de Revista/Publicación: | 2017 INTERNATIONAL CARNAHAN CONFERENCE ON SECURITY TECHNOLOGY (ICCST) |
| Fecha: | Diciembre 2017 |
| ISBN: | 978-1-5386-1585-0 |
| Materias: | |
| Palabras Clave Informales: | Anomaly detection, Gaussian mixture model, robust optical flow, video-surveillance |
| Escuela: | E.T.S.I. Telecomunicación (UPM) |
| Departamento: | Señales, Sistemas y Radiocomunicaciones |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
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.
Proyectos asociados
| Tipo | Código | Acrónimo | Responsable | Título |
|---|---|---|---|---|
| Gobierno de España | project RTC-2015-3527-1 | BEGISE | Sin especificar | Sin especificar |
Más información
| ID de Registro: | 50878 |
|---|---|
| Identificador DC: | http://oa.upm.es/50878/ |
| Identificador OAI: | oai:oa.upm.es:50878 |
| Identificador DOI: | 10.1109/CCST.2017.8167830 |
| URL Oficial: | https://ieeexplore.ieee.org/document/8167830/ |
| Depositado por: | Memoria Investigacion |
| Depositado el: | 29 May 2018 16:52 |
| Ultima Modificación: | 29 May 2018 16:52 |
