| Title | Person Detection with Deep Learning and IoT for Smart Home Security on Amazon Cloud |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Nazir, Sajid, Poorun, Yovin, Kaleem, Mohammad |
| Conference Name | 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) |
| Date Published | oct |
| Keywords | artificial intelligence, cloud computing, communications protocol, composability, Deep Learning, edge computing, edge detection, embedded programming, False Positive, Image edge detection, infrared sensors, Metrics, Motion detection, pubcrawl, remote monitoring, resilience, Resiliency, Scalability, security, Smart homes, web services |
| Abstract | A smart home provides better living environment by allowing remote Internet access for controlling the home appliances and devices. Security of smart homes is an important application area commonly using Passive Infrared Sensors (PIRs), image capture and analysis but such solutions sometimes fail to detect an event. An unambiguous person detection is important for security applications so that no event is missed and also that there are no false alarms which result in waste of resources. Cloud platforms provide deep learning and IoT services which can be used to implement an automated and failsafe security application. In this paper, we demonstrate reliable person detection for indoor and outdoor scenarios by integrating an application running on an edge device with AWS cloud services. We provide results for identifying a person before authorizing entry, detecting any trespassing within the boundaries, and monitoring movements within the home. |
| DOI | 10.1109/ICECCME52200.2021.9591027 |
| Citation Key | nazir_person_2021 |
Person Detection with Deep Learning and IoT for Smart Home Security on Amazon Cloud