Here is a clear, short, developer-friendly explanation of the system you want to build. We are building an AI-driven video monitoring platform that analyzes CCTV feeds in real time and executes user-defined commands. The system will also operate under a monthly subscription model, where each connected camera = a monthly fee. Core Objectives 1. Connect CCTV Cameras (IP cams, RTSP streams, NVR feeds). 2. Run AI analysis on each live feed (cloud or on-prem). 3. Allow users to type custom instructions/commands for each camera feed. 4. AI should interpret commands, monitor the feed, and perform actions. 5. Include alert automation (SMS, push, email). 6. Add shoplifting detection capabilities. AI Features Needed 1. Person Detection & Tracking • Use YOLOv8/v11, RT-DETR, or similar. • Track a person across frames and count how many times they appear. • Assign temporary IDs (e.g., Person_01). 2. Behavior Recognition • Loitering detection (e.g., “person passing here more than 5 times”). • Time-based triggers (e.g., “if someone appears after 12 a.m., alert me”). • Restricted zone detection. • Analytics (object count, heatmaps, etc.). 3. Shoplifting Detection AI must identify: • Concealing items in clothing/bag. • Taking items and passing exit without payment. • Sudden hand movements toward shelves. • Abnormal behavior patterns (loitering near shelf, avoiding staff, etc.). This uses: • Pose detection (MediaPipe, YOLO-pose). • Behavior classification (LSTM/Transformer models). • Object interaction detection (hand-to-object mapping). Command Interface (Rule Engine) Users should be able to type natural-language commands like: • “Feed 2: alert me if anyone appears after 12 a.m.” • “Feed 1: identify anyone who passes this area more than 5 times and give them a name.” • “Monitor all feeds for shoplifting and notify me immediately.” The system converts each command into machine-readable rules, such as: IF event = person_detected AFTER 00:00 THEN send_SMS or: IF track_id frequency > 5 THEN assign label "Frequent Visitor" The AI should use an LLM (OpenAI GPT, Llama 3, etc.) to: • Parse user requests • Convert them to rules • Manage triggers/actions System Architecture (Simplified) Frontend • Dashboard to view camera feeds • Section to type rules/commands • Notifications page • Subscription billing page Backend • API for camera management • Rule engine for interpreting user commands • Event processing system • Notification system (SMS/Email) • Subscription & authentication AI Processing Layer • Computer vision service (PyTorch/ONNX) • LLM service for command parsing • Shoplifting detection model • Tracking + event generation engine Storage • Short clips of events • Metadata (events, IDs, rules) • User profiles / billing Additional Notes • Must support multiple clients; each client sees only their own feeds. • System should scale (Kubernetes or serverless). • Use WebRTC or RTSP → HLS for live viewing. • Focus on GPU optimization for real-time performance. Subscription Logic • Each camera feed = billed entity. • Monthly recurring subscription (Stripe, Paystack, etc.). • Different tiers based on: • Number of cameras • Type of AI features enabled • Storage duration