What is a false positive in AI video analytics?

A false positive in AI video analytics occurs when the AI incorrectly identifies an event, object, or action as a security threat when no actual threat exists. The system generates an alarm based on this incorrect interpretation of video data.

For example, a false positive might occur when:

The AI mistakenly classifies a shadow, moving vegetation, or an animal as a person or vehicle.

Rain, snow, or reflections trigger an alarm due to misinterpretation by the system.

An object, such as a sign or a tree, is incorrectly identified as a human or vehicle.

False positives are a critical issue in video analytics as they can lead to “false alarm fatigue,” where staff may start ignoring alarms, potentially missing genuine threats.