# Reducing false positives

Tune your object filters to adjust false positives: `min_area`, `max_area`, `min_score`, `threshold`.

For object filters in your configuration, any single detection below `min_score` will be ignored as a false positive. `threshold` is based on the median of the history of scores (padded to 3 values) for a tracked object. Consider the following frames when `min_score` is set to 0.6 and threshold is set to 0.85:

Frame | Current Score | Score History | Computed Score | Detected Object
--- | --- | --- | --- | ---
1 | 0.7 | 0.0, 0, 0.7 | 0.0 | No
2 | 0.55 | 0.0, 0.7, 0.0 | 0.0 | No
3 | 0.85 | 0.7, 0.0, 0.85 | 0.7 | No
4 | 0.90 | 0.7, 0.85, 0.95, 0.90 | 0.875 | Yes
5 | 0.88 | 0.7, 0.85, 0.95, 0.90, 0.88 | 0.88 | Yes
6 | 0.95 | 0.7, 0.85, 0.95, 0.90, 0.88, 0.95 | 0.89 | Yes

In frame 2, the score is below the `min_score` value, so frigate ignores it and it becomes a 0.0. The computed score is the median of the score history (padding to at least 3 values), and only when that computed score crosses the `threshold` is the object marked as a true positive. That happens in frame 4 in the example.

Edit this page