Core Definition
Anomaly detection is a technology in which AI learns normal patterns and then detects, in real time, abnormal situations that deviate from those patterns, raising alerts. In a store environment, it is used to detect anomalous behavior such as intrusion during unattended hours, prolonged loitering, falls, and vandalism.
Store situations that need anomaly detection
In unmanned stores, convenience stores operating overnight, and warehouses or logistics centers, it is common to have no staff monitoring footage in real time. Even when an accident or theft occurs, it is often discovered only the next day, when someone checks the CCTV footage directly, which delays the response.
AI anomaly detection analyzes camera footage around the clock, and when a predefined abnormal behavior pattern is detected, it sends a text or app notification in real time. This makes real-time response possible even in the early morning hours while the operator is asleep.
How anomaly detection AI works
A deep-learning-based anomaly detection model learns thousands of hours of normal store footage to build a range of normal behavior. When it detects behavior such as staying in a specific zone longer than the allowed time, an unusual movement pattern, or a falling motion, it classifies the event as anomalous.
The difference from simple motion detection is that it filters for only "meaningful" anomalies. A banner swaying in the air conditioner's airflow or a delivery worker's normal movement is treated as normal, which minimizes false alarms.
How SAAI Uses It
SAAI saai care's anomaly detection feature provides a verified 98.2% detection accuracy in unmanned store and convenience store night operations. It supports industry-specific anomaly scenarios such as intrusion, loitering, falls, and refrigerated cases left open for extended periods, and automatically sends an app push notification when an anomaly is detected.
See it in action