Case Study: national retail chain improves video activity recognition with Moondream

National Retail Chain boosts action classification accuracy to 38.4% with Moondream

The customer, a national retail chain, faced the challenge of monitoring hundreds of surveillance camera feeds for loss prevention, which required a large security team. Human reviewers were limited by fatigue and could only watch a fraction of feeds at once, leading to missed incidents. They partnered with vendor Moondream to implement an automated video activity recognition solution using their Vision AI stack.

Moondream fine-tuned its base model using SFT training to accurately identify 174 distinct action categories in real time across all camera feeds. This solution improved action classification accuracy from 10.2% to 38.4%, enabling real-time flagging of events and significantly reducing security staffing requirements for video monitoring.