SaniaK27/AI-Survelliance-System

# AI-Based Surveillance System An intelligent AI-powered surveillance application developed using Computer Vision, Deep Learning, and Python to automate real-time monitoring, object detection, and activity analysis from live video streams. The system is designed to enhance modern surveillance operations by reducing manual monitoring efforts and enabling intelligent automated security analysis. ## Project Overview The AI-Based Surveillance System processes live video feeds from surveillance cameras and uses deep learning-based object detection techniques to identify, classify, and track objects such as humans, vehicles, and suspicious activities in real time. The project combines artificial intelligence and computer vision technologies to build an automated surveillance platform capable of continuous monitoring, event detection, and intelligent decision-making for modern security applications. The application was developed using Python, OpenCV, and deep learning frameworks to implement efficient real-time inference pipelines and scalable surveillance workflows. ## Features - Real-time video surveillance - Object detection and tracking - Suspicious activity monitoring - Live video stream processing - Automated event detection - AI-powered security analysis - Scalable modular architecture - Real-time inference workflows ## Technologies Used ### Programming Language - Python ### Computer Vision & AI - OpenCV - Deep Learning - YOLO / CNN-based Object Detection ### Libraries & Frameworks - NumPy - Pandas - TensorFlow / PyTorch ### Development Tools - VS Code - Jupyter Notebook - Git & GitHub ## System Workflow 1. Video Stream Input 2. Frame Extraction 3. Image Preprocessing 4. Object Detection 5. Object Tracking 6. Suspicious Activity Analysis 7. Real-Time Alert Generation 8. Monitoring Dashboard Output ## Project Architecture Surveillance Camera ↓ Video Stream Processing ↓ Image Preprocessing ↓ AI Object Detection Model ↓ Object Tracking & Analysis ↓ Real-Time Monitoring Output