Complex event detection in an intelligent surveillance system using CAISER platform
| Autor: | Leow Gaen Bein, Aini Hussain, Mohamad Hanif Md Saad, Rabiah Adawiyah Shahad |
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| Rok vydání: | 2016 |
| Předmět: |
Engineering
business.industry Template matching Real-time computing Confusion matrix Complex event processing 010103 numerical & computational mathematics 02 engineering and technology Intrusion detection system computer.software_genre 01 natural sciences Sequence pattern Smart surveillance 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining 0101 mathematics Raw data business Classifier (UML) computer |
| Zdroj: | 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES). |
| DOI: | 10.1109/icaees.2016.7888023 |
| Popis: | Interest about security and asset safety escalates due to the increasing crimes in this century. However, almost all existing surveillance systems have limited self-learning ability that only allow real time monitoring and are unable to identify the actual events that take place in the monitored ambient. As such, this research aims to implement a smart surveillance system with embedded Complex Event Processing (CEP) technology to assist the intrusion detection by correlating raw data extracted from different sources. Four classifiers are used in the CEP engine to predict the event occurrences from the raw data sequence pattern acquired from the door sensors and surveillance camera via intelligent rule template matching algorithm. Confusion matrix in terms of sensitivity, specificity and average detection accuracy as well as ROC plot are employed in classifier performance evaluation to quantify the efficiency of the surveillance system developed. |
| Databáze: | OpenAIRE |
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