Biblio

In this paper, an intruder detection system has been built with an implementation of convolutional neural network (CNN) using raspberry pi, Microsoft's Azure and Twilio cloud systems. The CNN algorithm which is stored in the cloud is implemented to basically classify input data as either intruder or user. By using the raspberry pi as the middleware and raspberry pi camera for image acquisition, efficient execution of the learning and classification operations are performed using higher resources that cloud computing offers. The cloud system is also programmed to alert designated users via multimedia messaging services (MMS) when intruders or users are detected. Furthermore, our work has demonstrated that, though convolutional neural network could impose high computing demands on a processor, the input data could be obtained with low-cost modules and middleware which are of low processing power while subjecting the actual learning algorithm execution to the cloud system.

Many surveillance cameras are using everywhere, the videos or images captured by these cameras are still dumped but they are not processed. Many methods are proposed for tracking and detecting the objects in the videos but we need the meaningful content called semantic content from these videos. Detecting Human activity recognition is quite complex. The proposed method called Semantic Content Extraction (SCE) from videos is used to identify the objects and the events present in the video. This model provides useful methodology for intruder detecting systems which provides the behavior and the activities performed by the intruder. Construction of ontology enhances the spatial and temporal relations between the objects or features extracted. Thus proposed system provides a best way for detecting the intruders, thieves and malpractices happening around us.