Home intruder detection system using machine learning and IoT
Home surveillance requires human effort, time and cost. Many tragedies such as robbery and vandalism occurred at home while the owners were negligent or not at home. Some residential areas hire guards to monitor their homes but hiring workers is not considered a cost-efficient option. Home Intrude...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
IIUM Press
2022
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/99110/13/99110_Home%20intruder%20detection%20system.pdf http://irep.iium.edu.my/99110/ https://journals.iium.edu.my/kict/index.php/IJPCC/article/view/329 |
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| Summary: | Home surveillance requires human effort, time and cost. Many tragedies such as robbery and
vandalism occurred at home while the owners were negligent or not at home. Some residential areas hire
guards to monitor their homes but hiring workers is not considered a cost-efficient option. Home Intruder
Detection System (HIDES) is an Internet of Things (IoT) system with a mobile application to help
homeowners in house surveillance by alerting users for any potential threats remotely. The main objectives
of HIDES are to create a reliable home security system with the implementation of IoT, to implement the
object detection algorithm to determine the presence of humans, and to develop a smart mobile application
for users to monitor their houses from anywhere in the world and be alerted if any threats are detected.
HIDES is developed using the System Development Life Cycle (SDLC) approach. HIDES implements an object
detection algorithm; Single-Shot Multibox Detection (SSD) in NVIDIA Jetson Nano to detect intruders
through a camera connected to the system. HIDES successfully achieves its objective in detecting persons
precisely and alerting the detection to users through mobile application remotely. The system can capture
video at an average of 20 frames per second (FPS) while detecting intruders and sending detection video to
the server. The mobile application achieves good performance where the loading time takes 2.3 seconds
while only requiring about 0.99MB of memory to run and 66.87MB of space. |
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