Edit: Sorry, should have read the rules first. Mods, if you take this down because its not tensorflow specific, I understand.
I’m just starting to play with neural networks, object detection, and tracking. I’m wondering what people use the confidence score of a detection for. Are there any common uses beyond simple confidence thresholding (i.e. output detection if conf > 0.5, otherwise dont)? Papers that use the confidence value in interesting ways are welcome!
For my own project, I was wondering how I might use the confidence score in the context of object tracking. For fun, and because its a super common application, i’ve been playing around with a traffic sign detector, and deploying it in a simulation. In the simulation, I get consistent and accurate predictions for real signs, and then frequent but short lived (i.e. 1-3 frame lifetime) false positives. I was thinking I could do some sort of tracking that uses the confidence values over a series of predictions to compute some kind of detection probability. I.e. if i look at a series of 30 frames, and in 20 i have 0.3 confidence of a detection, where the bounding boxes all belong to the same tracked object, then I’d argue there is more evidence that an object is there than if I look at a series of 30 frames, and have 2 detections that belong to a single object, but with a higher confidence e.g. conf=0.6. How can I leverage the confidence scores to create a more robust detection and tracking pipeline? Or am I already way off base (i’ve been trying to come up with a formula for how to do it, but probability and stochastics were never my strong suit and I know that the formulas I’ve been trying to write down implicitly assume independence, which I don’t know if that is the case here)?
Any way, how do you use the confidence values in your own projects?