| Title | Intruder Detection with Alert Using Cloud Based Convolutional Neural Network and Raspberry Pi |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Xenya, Michael Christopher, Kwayie, Crentsil, Quist-Aphesti, Kester |
| Conference Name | 2019 International Conference on Computing, Computational Modelling and Applications (ICCMA) |
| Keywords | Cameras, cloud based convolutional neural network, cloud computing, CNN algorithm, component, composability, convolutional neural nets, convolutional neural network, convolutional neural networks, Data models, feature extraction, image acquisition, intruder detection, intruder detection system, middleware, middleware security, MMS alert, Monitoring, policy-based governance, pubcrawl, raspberry p, Raspberry Pi, resilience, Resiliency, security of data, Training |
| Abstract | 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. |
| DOI | 10.1109/ICCMA.2019.00015 |
| Citation Key | xenya_intruder_2019 |
Intruder Detection with Alert Using Cloud Based Convolutional Neural Network and Raspberry Pi