Inventory & Security Monitoring
Computer vision is revolutionizing two critical areas: inventory management and security surveillance. Instead of manual counts and passive camera monitoring, AI enables proactive, automated real-time monitoring.
Shelf Scanning in Retail
The Problem
Out-of-stock situations cost retail globally over $1 trillion per year. A product not on the shelf doesn't get bought — the customer goes to a competitor.
AI-Powered Shelf Monitoring
How computer vision monitors shelves:
Fixed cameras:
- Mounted at strategic positions (shelf ends, ceiling)
- Continuous real-time monitoring
- Alert for empty shelf or wrong product
Mobile robots:
- Autonomous robots (e.g., Tally by Simbe Robotics, Badger Technologies) regularly scan shelves
- 3D vision for complete shelf capture
- Operate at night or during quiet hours
Employee apps:
- Smartphone-based shelf recognition
- Employee photographs shelf → AI analyzes in seconds
- Simplest and cheapest entry solution
What AI Detects
- Out-of-stock: Which products are missing? Since when?
- Planogram compliance: Are products in the right place?
- Price tags: Do price labels match the system?
- Facing count: How many products face forward?
- Competitor monitoring: Which competing products are placed nearby?
Results
- Out-of-stock reduction: 30–50%
- Revenue increase: 2–5% through better shelf maintenance
- Staff efficiency: Inventory check in 10 minutes instead of 4 hours
Video Surveillance with AI
From Passive to Proactive
Traditional video surveillance: A security guard watches 20 screens simultaneously — and misses 95% of relevant events. AI video analysis automatically detects:
- Perimeter protection: Person enters restricted area → immediate alert
- Loitering detection: Person staying unusually long at a location
- Crowd detection: Gatherings posing a security risk
- Vehicle recognition: Unknown vehicles, illegally parked trucks
- Object detection: Abandoned bags, packages in sensitive areas
Industrial Safety
In production environments:
- PPE detection: Is the employee wearing helmet, safety glasses, vest?
- Zone monitoring: Is someone entering a danger zone while machines are running?
- Falls: Automatic fall detection (person lying on the ground)
- Early fire detection: Detect smoke and flying sparks before fire alarms trigger
Anomaly Detection
Detecting Unusual Behavior
AI learns the "normal state" and flags deviations:
- Movement patterns: Employee who normally takes route A suddenly goes to area B
- Temporal anomalies: Activity outside business hours
- Object changes: Something was moved, added, or removed
- Behavioral changes: Hectic behavior, running instead of walking
Data Privacy and Ethics
GDPR Requirements:
- Transparency: Signage, privacy policy
- Legal basis: Legitimate interest (Art. 6(1)(f)) — balanced against personality rights
- Storage limitation: Delete recordings after 48–72 hours (except for incidents)
- No biometric tracking: Facial recognition in public areas is heavily restricted in the EU (EU AI Act)
Best Practices:
- Privacy by design: People tracking only anonymized (heatmaps instead of individuals)
- Involve works council: Complete works council agreement before introduction
- Purpose limitation: Use data only for defined security purpose
Important: Technical capabilities often exceed legal boundaries. Just because computer vision can do something doesn't mean you're allowed to.