Live Camera Analytics for Retail Loss Prevention

Added Jun 2025 3 design docs

Retailers lose billions annually to theft and also lack basic visibility into how shoppers move through their stores. Camera infrastructure is already installed in most locations, but the footage is rarely analyzed in real time, so suspicious activity is discovered only after the fact and merchandising decisions are made without traffic data. In this project the intern builds a real-time video intelligence system on Flask and OpenCV that consumes live camera feeds and runs YOLO and SSD detection models to identify people, products, and unusual activity patterns as they happen. The work includes an analytics dashboard that renders occupancy counts, dwell-time heatmaps of store zones, and a configurable event alerting flow that notifies staff when defined conditions are detected. NumPy and pandas support frame processing and aggregate analytics, and the system is packaged with Docker, with model optimization work such as reduced input resolutions and lighter network variants so inference can run on edge devices near the cameras. Completing the build teaches the intern how to sustain detection throughput on streaming video, how to translate raw detections into operational metrics a store manager can act on, and how to deploy computer vision workloads outside the data center.

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