Machine Learning-Based Home Surveillance System for Enhanced Security
Objective
To design and develop a machine learning-based home surveillance system for enhanced security and real-time monitoring. To detect and identify intrusions or suspicious activities using intelligent algorithms, while providing timely alerts and improving overall safety and reliability of smart home environments.
Abstract
This project presents a Machine Learning-Based Home Surveillance System for Enhanced Security, designed to provide intelligent monitoring and access control. The system is built using a Raspberry Pi integrated with a web camera, ultrasonic sensor, GSM module, buzzer, and LED indicators. The camera captures real-time video, and the LBPH (Local Binary Pattern Histogram) algorithm is used for face recognition to classify authorized and unauthorized persons.The ultrasonic sensor is used to detect the presence of a person near the entry point. If an authorized person is detected, access is granted and a notification email is sent. If an unauthorized person is identified, the system triggers a buzzer alert, activates a red LED, and sends an alert message via the GSM module.This system enhances home security by combining real-time monitoring, machine learning-based classification, and instant alert mechanisms. It provides a reliable and efficient solution for smart home surveillance and access control applications.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Hardware components:
Raspberry Pi
Memory Card
USB Web Camera
Ultrasonic Sensor
GSM Module
Buzzer
Red LED
Power Supply
Adapter
Software components:
Python
Rasbian OS
Learning Outcomes
- Understand Raspberry Pi architecture and GPIO configuration
- Learn how to install and configure Raspbian OS and required Python libraries
- Interface analog sensors with Raspberry Pi using MCP3008 ADC
- Implement image classification using Artificial Neural Networks
- Develop real-time skin analysis using USB camera input
- Build automated health screening systems with display and alert features
- Integrate temperature and heartbeat monitoring in diagnostic systems
- Analyze and interpret classification output for healthcare applications
- About Project Development Life Cycle:
- Planning and Requirement Gathering (software’s, Tools, Hardware components, etc.,)
- Schematic preparation
- Code development and debugging
- Hardware development and debugging
- Development of the Project and Output testing
- Practical exposure to:
- Hardware and software tools.
- Solution providing for real time problems.
- Working with team/ individual.
- Work on Creative ideas.
- Project development Skills:
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
Paper Publishing
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6-2-85/B, Old Maternity Hospital Road, Thyagaraja Nagar, Tirupati, Andhra Pradesh – 517501
+91 9030333433
+91 9393939065
0877-2261612
Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
