# How to model the probability of detecting an image, given it is seen multiple times

Are there any existing methods/models describing the probability of an object being detected by a computer vision algorithm given it is seen $$n$$ times at similar angles and orientations? I know that an autonomous car may, for example, have trouble recognizing a stop sign and as a result the image recognition system being used may have a bounded box around the stop sign continuously appearing and disappearing signaling that the object recognition algorithm is only detecting the stop sign a certain amount of the time it sees it. I would like to understand this phenomenon in a more general sense.

More precisely put, I would like to model that: given an object is detected with probability $$p$$ if it is seen 'once' (AKA during a moment in time), then what is the probability of it being detected if it is seen for a second time, a third time, etc... Each 'moment in time' may be characterized by a single video frame/image or some other unit of time. Each time the image is seen it may be in a similar but not identical orientation.

• the missing link here, i think, is the identification of the object seen multiple times as the same object in time, instead of as a different (instance of an) object. As a sidenote camshift algorithm can track an object once it is detected (even if it is hidden in some frames) Apr 1 at 18:52