Semantic web technologies for video surveillance metadata

Video surveillance systems are growing in size and complexity. Such systems typically consist of integrated modules of different vendors to cope with the increasing demands on network and storage capacity, intelligent video analytics, picture quality, and enhanced visual interfaces. Within a surveillance system, relevant information (like technical details on the video sequences, or analysis results of the monitored environment) is described using metadata standards. However, different modules typically use different standards, resulting in metadata interoperability problems. In this paper, we introduce the application of Semantic Web Technologies to overcome such problems. We present a semantic, layered metadata model and integrate it within a video surveillance system. Besides dealing with the metadata interoperability problem, the advantages of using Semantic Web Technologies and the inherent rule support are shown. A practical use case scenario is presented to illustrate the benefits of our novel approach

A semantic autonomous video surveillance system for dense camera networks in smart cities

Producción CientíficaThis paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement. The system is designed to minimize video processing and transmission, thus allowing a large number of cameras to be deployed on the system, and therefore making it suitable for its usage as an integrated safety and security solution in Smart Cities. Alarm detection is performed on the basis of parameters of the moving objects and their trajectories, and is performed using semantic reasoning and ontologies. This means that the system employs a high-level conceptual language easy to understand for human operators, capable of raising enriched alarms with descriptions of what is happening on the image, and to automate reactions to them such as alerting the appropriate emergency services using the Smart City safety network

Caracterización semántica de espacios: Sistema de Videovigilancia Inteligente en Smart Cities

Esta Tesis Doctoral, realizada dentro del proyecto europeo HuSIMS - Human Situation Monitoring System, presenta una metodología inteligente para la caracterización de escenarios capaz de detectar e identificar situaciones anómalas analizando el movimiento de los objetos. El sistema está diseñado para reducir al mínimo el procesamiento y la transmisión de vídeo permitiendo el despliegue de un gran número de cámaras y sensores, y por lo tanto adecuada para Smart Cities. Se propone un enfoque en tres etapas. Primero, la detección de objetos en movimiento en las propias cámaras, utilizando algorítmica sencilla, evitando el envío de datos de vídeo. Segundo, la construcción de un modelo de las zonas de las escenas utilizando los parámetros de movimiento identificados previamente. Y tercero, la realización de razonado semántico sobre el modelo de rutas y los parámetros de los objetos de la escena actual para identificar las alarmas reconociendo la naturaleza de los eventosDepartamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemátic