Intelligent surveillance system with integration of heterogeneous information for intrusion detection

Recently, interest about security in public and private spaces has increased in favour of social welfare. Surveillance systems are increasingly needed to provide security for citizens and infrastructures. Currently there are many buildings that are equipped with cameras, sensors or microphones. However, it is difficult to find tools that integrate the information from these sources in a homogeneous system. On the other hand, the intruder detection is increasingly demanded in the corporate, commercial or private sector. For these reasons, we propose a multi-sensor intelligent system that uses information from several sources analysis (video, audio and other sensors) to identify dangerous or interest intrusions. So, we have designed a generic ontology that allows to integrate in a homogeneous way all the input heterogeneous knowledge. To perform the intrusion analysis, we propose a rule-based model, which process all the information obtained from the monitored environment. This model is easily customizable and adjustable, since the rules that define an intrusion in a semantic way can be configured depending on the scenario and circumstances. The system generates an alarm whenever an intrusion is detected. Besides, this alarm is also notified via mobile devices. So, the system reports in real time according to device capabilities, generating a context-sensitive notification.

► We propose a multi-sensor intelligent system to identify dangerous intrusions.
► The system uses as input: video and audio analysis and sensor information.
► We have designed an ontology to integrate in a homogeneous way all the input heterogeneous knowledge.
► We have developed a customizable and adjustable rule-based model to perform the intrusion analysis.
► The alarm notification is context-sensitive and in real time.