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A Major Retail Chain
Retail Security AI System
97% Detection Accuracy
The Challenge
The client was losing over $2M annually to retail theft and needed a scalable, real-time monitoring solution that could process feeds from 50+ cameras simultaneously without false alarm fatigue.
Our Approach
We built a custom computer vision pipeline using optimized deep learning models for real-time object detection and behavioral analysis. The system processes concurrent camera streams with edge inference for low-latency alerts, backed by a cloud-based analytics dashboard for loss prevention teams.
Results
- 97% detection accuracy across all monitored zones
- 50+ concurrent camera streams processed in real-time
- $2M annual loss prevention savings
- Sub-second alert latency for security team response
Tech Stack
PythonTensorFlowComputer VisionAWSDockerWebSocket