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Smart Security Camera System Edge AI Solution

Real-time person detection and facial recognition system deployed on edge devices for intelligent security monitoring with 98.9% accuracy and sub-100ms response time.

Project Overview

Developed an advanced edge AI security camera system capable of real-time person detection, facial recognition, and behavioral analysis. The system processes video streams locally on embedded hardware, eliminating the need for cloud connectivity while maintaining high accuracy and low latency.

Hardware

NVIDIA Jetson Nano, Raspberry Pi 4, Custom ARM SoC

AI Framework

TensorFlow Lite, OpenVINO, ONNX Runtime

Computer Vision

OpenCV, YOLOv7, FaceNet, DeepSORT

Deployment

Docker, Kubernetes, Edge Computing

The Challenge

A leading security company needed to upgrade their surveillance infrastructure with AI-powered capabilities while maintaining privacy and reducing bandwidth costs. Key requirements included:

  • Real-time person detection with 95%+ accuracy
  • Facial recognition for authorized personnel
  • Behavioral analysis for suspicious activity detection
  • Local processing to ensure data privacy
  • Integration with existing security infrastructure
  • Cost-effective deployment across 500+ locations

Our Solution

1. Custom AI Model Development

Developed a lightweight YOLOv7-based person detection model optimized for edge deployment. The model was quantized to INT8 precision, reducing model size by 75% while maintaining 98.9% accuracy.

2. Facial Recognition Pipeline

Implemented a FaceNet-based recognition system with custom training on client's personnel database. The system achieves 98.2% accuracy in controlled lighting conditions and 94.3% in challenging outdoor environments.

3. Edge Optimization

Optimized the entire pipeline for ARM-based processors using TensorFlow Lite and OpenVINO. Achieved inference times of 45ms per frame on NVIDIA Jetson Nano and 78ms on Raspberry Pi 4.

4. Real-time Processing

Implemented multi-threaded processing pipeline with frame buffering and intelligent scheduling to maintain 30 FPS processing while handling multiple concurrent streams.

Technical Implementation

Architecture

The system consists of three main components:

  • Capture Module: Handles video input from IP cameras and USB devices
  • AI Processing Engine: Runs detection and recognition models
  • Alert System: Manages notifications and integration with security systems

Model Optimization

Applied advanced optimization techniques including:

  • Model pruning to remove redundant parameters
  • Quantization to reduce precision from FP32 to INT8
  • Knowledge distillation for model compression
  • TensorRT optimization for NVIDIA hardware

Results & Impact

98.9%

Person Detection Accuracy

45ms

Average Inference Time

75%

Bandwidth Reduction

500+

Deployed Locations

Business Impact

  • Reduced false alarms by 85% through intelligent filtering
  • Decreased bandwidth costs by 75% with local processing
  • Improved response time by 60% with real-time alerts
  • Enhanced security coverage with 24/7 automated monitoring
  • Scalable solution supporting future expansion

Future Enhancements

Planned improvements include:

  • Multi-camera tracking across different locations
  • Advanced behavioral analysis for threat assessment
  • Integration with IoT sensors for comprehensive monitoring
  • Mobile app for real-time alerts and management
  • Cloud analytics for pattern recognition and insights