SaaS
AI-Powered Security Surveillance System
Real-time AI detection and monitoring for residential societies with YOLOv8 person detection
Industry
Residential Security
Timeline
4 months from project kickoff to production deployment
Team Size
5 engineers (2 ML/AI specialists, 2 full-stack, 1 DevOps)
Technologies
10+
Overview
A residential society management company needed an advanced security surveillance system that could automatically detect and alert on security incidents. The system needed to support multiple camera types (main gate, boundary wall, society rules), provide real-time monitoring for guards, and automatically log suspicious activities using AI-powered detection.
The Challenge
The residential society faced significant security challenges: • Manual monitoring of multiple cameras was inefficient and error-prone • Guards couldn't monitor all cameras simultaneously • No automated detection of boundary violations or intrusions • Difficulty tracking vehicle entry/exit and license plates • Lack of historical data for security incident analysis • No way to assign specific cameras to specific guards • High false alarm rates from motion detection systems • Need for real-time alerts on suspicious activities • Requirement for audit trails and activity logging • Scalability concerns as the society expanded
Our Solution
We built a comprehensive AI-powered security surveillance system: AI Detection Engine: • YOLOv8l model for high-accuracy person detection • ByteTrack multi-object tracking across video frames • Real-time boundary crossing detection with configurable zones • Intrusion detection for restricted areas • Climbing detection near walls and fences • Loitering detection (persons staying >60 seconds) • Crowd detection alerts (≥5 persons gathering) • GPU-accelerated inference with ONNX optimization • Vehicle detection and classification (ready for expansion) • License plate OCR capabilities (ready for integration) Camera Management: • Support for three camera types: Main Gate, Boundary Wall, Society Rule • RTSP stream integration with connection testing • Real-time MJPEG streaming with detection overlays • Camera status monitoring and health checks • Configurable detection zones and boundary lines • Multi-camera assignment to guards Guard Management: • Hierarchical access control (Super Admin and Guards) • Guard-specific camera assignments • Guard dashboard with assigned camera feeds • Activity timeline and alert management • Vehicle logging interface for guards Real-Time Monitoring: • Live video streaming with AI detection overlays • Real-time alerts for security incidents • Activity timeline showing all detected events • Alert detail modals with captured images • Camera status grid showing all stream health Vehicle Logging: • Manual vehicle entry/exit logging • License plate number tracking • Vehicle image capture and storage • Search functionality by plate number • Statistics and reporting Infrastructure: • FastAPI backend with async processing • PostgreSQL database with SQLAlchemy ORM • Docker containerization for easy deployment • Nginx reverse proxy for production • GPU support for accelerated AI inference • Scalable architecture supporting 50+ cameras
Technologies Used
Impact & Results
95%
Detection Accuracy
< 100ms
Detection Latency
85%
False Alarm Reduction
60% faster
Security Response Time
50+
Cameras Supported
98%
Incident Detection Rate
The AI detection system has revolutionized our security operations. We can now monitor all cameras effectively with fewer guards, and the automated alerts ensure we never miss a security incident. The system is incredibly accurate and has significantly reduced false alarms.
Ahmed Khan
Security Manager, Residential Society Management
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