Person Detection with Deep Learning and IoT for Smart Home Security on Amazon Cloud
- Resource Type
- Conference
- Authors
- Nazir, Sajid; Poorun, Yovin; Kaleem, Mohammad
- Source
- 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2021 International Conference on. :1-6 Oct, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Deep learning
Cloud computing
Web services
Image edge detection
Smart homes
Infrared sensors
Security
embedded programming
remote monitoring
edge computing
motion detection
communications protocol
artificial intelligence
false positive
- Language
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.