The accuracy of intelligent analysis continues to improve, and the false positive rate has become a key indicator for project acceptance
Algorithm iteration and scenario-based optimization have greatly improved the usability of behavior recognition, and the false alarm rate has changed from a major pain point in the past to a quantifiable and acceptable indicator.
Where do false positives come from
- Small animals, leaves, and changes in light and shadow trigger target detection;
- The detection area spans roads or entrances and exits, and normal activities are considered abnormal;
- Low illumination at night results in incomplete target features;
- Sensitivity is one-size-fits-all, without distinguishing between scenes.
Optimization method
- Refined detection area: Avoid roads, trees and reflective surfaces.
- Target type filtering: Only alarm targets that match humanoid/vehicle-shaped characteristics.
- Dual verification: Video + radar/infrared combination judgment.
- Time policy: Distinguish day/night, weekday/holiday rules.
- On-site tuning: On-site adjustments for 1–2 weeks after launch.
How to write acceptance criteria
It is recommended to clarify in the project acceptance terms: the upper limit of false alarms in normal scenarios, the detection rate of typical events, The response time from alarm to screen pop-up makes the indicator quantifiable.
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