AI Empowered Security Monitoring System with the Help of Deployed ML Models
DOI:
https://doi.org/10.15680/IJCTECE.2026.0901011Keywords:
Artificial Intelligence, Machine Learning, Computer Vision, Security Monitoring, Intruder Detection, Face Recognition, OpenCV, TensorFlow, PyTorch, MediaPipe, YOLO Algorithm, Video Surveillance, Real-time Detection, Flask, Django, Automated Alert System, Deep Learning, PygameAbstract
This project introduces a fully software-based intelligent surveillance system that leverages Artificial Intelligence (AI) and Machine Learning (ML) models to automate video monitoring, detect intruders, suspicious movements, and faces in real time. Traditional surveillance systems rely heavily on manual observation, which is time-consuming and prone to human error. To overcome these limitations, the proposed system integrates computer vision techniques with Python libraries such as OpenCV, TensorFlow, PyTorch, and MediaPipe to deliver a smart, responsive solution.
The system processes live or recorded video feeds, drawing bounding boxes around detected objects or faces and triggering audio alerts using pygame with a cooldown mechanism to prevent repeated notifications. This ensures efficient monitoring while minimizing false alarms. For enhanced accessibility, the solution can be deployed with Flask or Django, enabling real-time visualization and remote monitoring through a web interface. Since the system is entirely software-driven, it eliminates the need for external hardware components, making it cost-effective, scalable, and adaptable to diverse environments such as offices, laboratories, and residential areas. By combining lightweight ML models, real-time detection, and automated alerting, this project demonstrates how AI can minimize human effort, reduce error, and enhance security through a smart, fully software-driven approach.
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