Visible to the public IoT Based Smart Video Surveillance System Using Convolutional Neural Network

TitleIoT Based Smart Video Surveillance System Using Convolutional Neural Network
Publication TypeConference Paper
Year of Publication2020
AuthorsKhudhair, A. B., Ghani, R. F.
Conference Name2020 6th International Engineering Conference “Sustainable Technology and Development" (IEC)
Date PublishedFeb. 2020
ISBN Number978-1-7281-5910-2
Keywordscamera, captured video, client and server architecture, client-server systems, CNN, COCO dataset, common objects in context dataset, convolutional neural nets, convolutional neural network, crime rate, Deep Learning, deep video, fully automatic video surveillance systems, image capture, Internet of Things, IoT, IoT based smart video surveillance system, learning (artificial intelligence), Metrics, microprocessor chips, Mobile Application, mobile computing, MobileNetv2-SSDLite model, object detection, object detection model, police data processing, pubcrawl, Raspberry Pi, resilience, Resiliency, Scalability, Smart video surveillance, SMS notification, video signal processing, video surveillance, Web application

Video surveillance plays an important role in our times. It is a great help in reducing the crime rate, and it can also help to monitor the status of facilities. The performance of the video surveillance system is limited by human factors such as fatigue, time efficiency, and human resources. It would be beneficial for all if fully automatic video surveillance systems are employed to do the job. The automation of the video surveillance system is still not satisfying regarding many problems such as the accuracy of the detector, bandwidth consumption, storage usage, etc. This scientific paper mainly focuses on a video surveillance system using Convolutional Neural Networks (CNN), IoT and cloud. The system contains multi nods, each node consists of a microprocessor(Raspberry Pi) and a camera, the nodes communicate with each other using client and server architecture. The nodes can detect humans using a pretraining MobileNetv2-SSDLite model and Common Objects in Context(COCO) dataset, the captured video will stream to the main node(only one node will communicate with cloud) in order to stream the video to the cloud. Also, the main node will send an SMS notification to the security team to inform the detection of humans. The security team can check the videos captured using a mobile application or web application. Operating the Object detection model of Deep learning will be required a large amount of the computational power, for instance, the Raspberry Pi with a limited in performance for that reason we used the MobileNetv2-SSDLite model.

Citation Keykhudhair_iot_2020