Computer Vision for Retail Environments
Requires basic Python knowledge and access to a computer with GPU
What You'll Learn
Cameras in stores have been recording footage for decades. Computer vision turns that footage into actionable data.
This program focuses on three main applications: shelf monitoring, foot traffic analysis, and security improvements. You will learn how systems detect out-of-stock items, track customer movement patterns, and identify suspicious behavior without invading privacy.
The technical work involves convolutional neural networks, object detection, and tracking algorithms. We use OpenCV and YOLO for hands-on projects. You will build a shelf monitoring prototype that counts products and detects empty spaces, similar to systems used by major retailers.
We spend significant time on practical constraints: lighting variations, camera placement, processing costs, and accuracy requirements. You will also learn about privacy regulations like GDPR and CCPA, because ignoring them creates legal problems.
This is not theoretical computer science. Every topic connects to real retail problems with measurable outcomes.
Program Details
Course Modules
Computer Vision Basics
- Image processing fundamentals for retail cameras
- Object detection and classification methods
- Working with video streams and frame analysis
- Handling lighting and environmental variations
Retail Applications
- Shelf Monitoring Systems
- Product detection, planogram compliance, out-of-stock alerts, misplaced item identification
- Customer Analytics
- Foot traffic heatmaps, dwell time measurement, queue length detection, demographic analysis basics
- Loss Prevention
- Anomaly detection, behavior pattern analysis, self-checkout monitoring
Implementation Projects
- Build a shelf monitoring system from scratch
- Create a foot traffic analyzer using existing footage
- Deploy a model to edge devices for real-time processing
Final project involves optimizing a computer vision system for retail constraints: cost, speed, and accuracy tradeoffs