I work in the AV space. There's a lot of ambiguity in labeling.
How should a crowd of people be annotated? A line of parked cars? A photograph of a car?
Examine the training set!
How should a crowd of people be annotated? A line of parked cars? A photograph of a car?
Examine the training set!
I.e. if during training a ML system notices the ambiguous combination (i.e. a woman pushing a baby stroller or a crowd) and marks it as a pedestrian, then it gets penalized in a manner that teaches it to ignore these ambigious combinations and treat it as nothing; while in practice it should probably treat such ambiguous combinations as even more "avoid-worthy" as an ordinary pedestrian.
The problem is that the default assumption is "clear road, you can drive there" - so what we need isn't "pedestrian detection" that finds pedestrians and only pedestrians, we need detection of random stuff that you shouldn't drive over. If a kid is wearing a weird Halloween costume, that doesn't look like a pedestrian, but it is one; If somebody has set up a tent in the middle of a supermarket parking lot, that's not a pedestrian but it should be avoided just like one.
Sooner or later it'd be nice to be able to drive one of these things in a place that isn't southern California.