Loss Prevention & Theft Detection

The Problem

Retail shrinkage costs the industry over $100 billion annually. Security staff can only monitor a fraction of camera feeds simultaneously. Reviewing footage after the fact is reactive — the loss has already occurred. Organized retail crime is increasing in sophistication, and traditional loss prevention methods are not keeping pace.

How Computer Vision Solves It

Computer vision analyzes existing security camera feeds in real time, detecting behavioral patterns associated with theft — concealment, basket-stuffing, ticket switching, and coordinated distraction techniques. Alerts are generated for security staff to intervene during the event rather than after.

The Challenge with Current Solutions

Distinguishing suspicious behavior from normal shopping is nuanced and context-dependent. False alerts on legitimate customers damage the shopping experience. Camera coverage gaps create blind spots. Privacy regulations vary by jurisdiction and limit how video analytics can be applied.

What vfrog Brings That's Different

vfrog trains on your specific store layouts and common loss scenarios. Models focus on behavioral patterns rather than individual identification, supporting privacy compliance. Synthetic data covers rare theft techniques. Alert thresholds are calibrated per location to minimize false positives.

Key Benefits

• Real-time alerts during theft events, not after
• Reduced shrinkage without additional security staffing
• Behavioral detection that respects customer privacy
• Coverage across all cameras simultaneously
• Location-specific calibration for fewer false alerts
• Shrinkage pattern analytics for prevention strategy