epic-surveillance

byJanvi shah

Scope of Work Project: AI-Powered Hybrid Surveillance & Remote Monitoring Platform Client: American Global Security (AGS) – SiteWatch Technology Prepared By: fxis.ai 1. Project Objective To design and develop a scalable, AI-powered hybrid (Edge + Cloud) surveillance system capable of: Monitoring 1,000+ cameras initially (scalable to 5,000–10,000+) Providing real-time intelligent detection Delivering automated voice response (AI agent-based intervention) Enabling centralized dashboard-based monitoring Reducing manual security oversight Supporting proactive threat prevention 2. System Architecture Overview 2.1 Hybrid Infrastructure Model As discussed in the meeting AI-Survilliance-System-Discussi… , the system will use: Edge Layer (On-site – Jetson or equivalent GPU devices): Real-time object detection Motion tracking Zone monitoring Immediate voice response trigger Cloud Layer (Server-side AI Processing): Advanced analytics LLM-based contextual reasoning Event classification Centralized dashboard Alert management Data storage & historical analysis This ensures: Fast response at edge Scalability and advanced intelligence via cloud Optimized infrastructure cost-performance balance 3. Core Functional Modules 3.1 Camera & Device Management Module Support for multi-camera units (7 cameras + IP speaker per unit) Device onboarding & provisioning Health monitoring of devices Remote firmware update capability Camera grouping by site 3.2 AI Detection & Event Recognition Engine The system will detect: Trespassing Detection Unauthorized entry detection Person tracking within defined restricted zones Real-time alert trigger Loitering Detection Time-based presence detection within a zone Threshold-based alert system Zone-Based Monitoring Virtual zone creation (e.g., doors, restricted areas) Rule-based triggers Door State Monitoring Detect door open/closed state Alert if door remains open beyond defined time Auto voice reminder to close door Custom Rule Engine Client-defined event configurations Future extensibility for additional use cases 3.3 AI Voice Response System (AI Agent Layer) Integration with IP speakers Dynamic AI-generated announcements Context-aware verbal warnings such as: “You are trespassing.” “Please leave the restricted area.” “Please ensure the door is locked.” LLM-based natural language generation (cloud-assisted) Event-specific scripted + dynamic responses 3.4 Notification & Alert System Real-time email notifications SMS integration (optional phase) Dashboard alert center Escalation workflow (e.g., notify security dispatch) Alert logs and audit trails 3.5 Central Monitoring Dashboard Web-based dashboard including: Live camera feed view Event timeline AI-detected events summary Multi-site overview User role management (Admin / Operator) Alert filtering and search Historical playback & analytics Enhanced UI/UX (competitive advantage over Spot AI as discussed AI-Survilliance-System-Discussi… ) 3.6 Scalability Framework Designed for 1,000 cameras (Phase 1) Infrastructure blueprint scalable to 5,000–10,000+ cameras Modular microservices architecture Horizontal scaling via cloud infrastructure 4. AI & Technical Stack (Proposed) Edge Layer NVIDIA Jetson (Or equivalent edge GPU) OpenCV / TensorRT optimization Lightweight object detection models (YOLO variant) Cloud Layer Scalable backend (Python/FastAPI or Node.js) LLM integration for contextual reasoning Event processing engine PostgreSQL / Time-series DB Cloud storage (AWS / GCP / Azure – TBD) Dockerized microservices 5. Phased Development Plan Phase 1 – Architecture & Infrastructure Design System architecture blueprint Edge-cloud workload split Data flow diagram Security architecture Phase 2 – Core AI Detection Module Person detection Trespassing logic Zone creation Door detection model Phase 3 – Voice AI Integration Speaker communication module Context-based announcement engine Response latency optimization Phase 4 – Dashboard Development Admin panel Live monitoring Alert management Analytics view Phase 5 – Pilot Deployment Limited site testing Performance benchmarking Model fine-tuning Phase 6 – Scale Optimization Load testing Multi-site rollout readiness Security hardening 6. Deliverables Complete hybrid AI surveillance system Web dashboard Edge AI module (deployable on Jetson) Voice response integration API documentation Deployment documentation