robmarkcole
Retrying to exclude false positives
I'm getting high confidence false positives sometimes when detecting humans, it can either be caused by a shadow, a dog, or others. I am wondering if there is an easy way to filter those out. For example - have a configuration parameter indicating how many frames are sent to the API before the detection can be trusted. If my dog looks 80% like a human in one frame, but then it's missing in the second - probably it was a false positive. Can we implement something like that?
I’m for this as well. I’m currently using a combo of frigate, DeepStack, CompreFace, and Double Take, and I still get false positives. With only 3 models trained DeepStack over CompreFace usually has the wrong entry. Thus the reason I added another detector to check against. That way both detectors have to come back positive before the rest of my automations move.
On Jan 20, 2022, at 3:13 AM, grinco @.***> wrote:
I'm getting high confidence false positives sometimes when detecting humans, it can either be caused by a shadow, a dog, or others. I am wondering if there is an easy way to filter those out. For example - have a configuration parameter indicating how many frames are sent to the API before the detection can be trusted. If my dog looks 80% like a human in one frame, but then it's missing in the second - probably it was a false positive. Can we implement something like that?
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In general, you need to raise the confidence threshold, say to 90%
Ensemble approach is interesting, am makes sense if some models perform better under different conditions, e.g. at night. However managing the ensemble can be complex
