Thanks thats great. It’s understandable that people switch as the accuracy was better. However, that should be put into context and into perspective if your goal is to detect people for security purposes with low false positives. For example (anecdotally from a low number of tests), when analysing one of my false positives using YOLOV4 I notices that yolov4 correctly detected many more flower pots in an image than yolov3 PJ Reddie detected. That will help improve the overall score of YOLOV4 but not in a manner that is relative to people detection for security. Again anecdotally it appears that YOLOV4 is a little more eager to see things and as such this results in correctly seeing more objects (Perhaps non-people objects) than PJ Reddie YOLOV3, which all goes to increase it’s accuracy. It’s possible that this behavior results in more correct matches of small people images as well (i.e. people far away), which increases the overal score.
However, if I had to choose to get twice as many correct matches of people far away that would normally be missed against getting a few extra false positives close up. For security I would choose to get less false positives close up as far away is less of a threat but false positives really mess up the whole process of monitoring.
I hope my explanation make’s sense. It’s really an attempt to say that overall accuracy score is not the only thing that matters when choosing an algorithm for security and why the PJ Reddie algorithm is still very relevant in the security field.
In my up coming software release I’ll be allowing the user to choose one of three algorithms so they can choose (And test) for themselves which suites them better. In short, if the view of view is non-cluttered and doesn’t contain much noise that can trigger false positives, then it’s quite viable to choose for the faster algorithm. In very cluttered environments, where you do not want to make too many exclusion zones, then choosing the algorithm with a lower incidence of false positives is likely a better choice.
Hope this helps.