Reducing false positives

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

The min_area and max_area values are compared against the area (number of pixels) from a given detected object. If the area is outside this range, the object will be ignored as a false positive. This allows objects that must be too small or too large to be ignored.

Similarly, the min_ratio and max_ratio values are compared against a given detected object's width/height ratio (in pixels). If the ratio is outside this range, the object will be ignored as a false positive. This allows objects that are proportionally too short-and-wide (higher ratio) or too tall-and-narrow (smaller ratio) to be ignored.

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.