The best rule with efficientdet_lite0 has been hard coding the number of animals in frame & making sure that number is always visible so it doesn't get false positives.  Tracking just the highest score would oscillate between 2 animals.  Since lions are poor, testing would require either printing out a 2nd animal or just testing with 1 animal & living with errors.

Been finding efficientdet to be not accurate enough to have any deadband.  The mane rule has been to keep the top of the box 7% from the top of the frame with no deadband.  If the total height of the box is under 75% of the screen height, center the entire box in the frame regardless of head position.

Minimum distance from the top must be greater than the deadband or it won't tilt up.  Another problem is if the box is under 75% but the head is above the frame, it won't tilt up in some cases.  That might be a matter of deadband.  If the difference between 75% & 7% is too low, the box will oscillate between being 75% of the screen height & 7% from the top.

Most of the time, the box is the full frame height, so it uses the head rule.  To get under 75% of the screen height, the error rate has to go up.

Ideally, efficientdet could be trained on videos which have been labeled with COCOv5, but all the videos with the desired body positions are shot in sleezy hotel rooms & they're very addicting to edit.  The neural net needs a wide variety of backgrounds besides hotel rooms to isolate the human forms.

5 hours on the GTX 970M yielded a new model trained just on hotel videos.  It was a total failure compared to the COCO training set.

Animals have long dreamed of software that could synthesize unlimited amounts of a certain type of video.  That's basically why there are so many attempts at making synthetic videos which are so bad, they're called neural network dream generators.  The best we can do is software that points the camera.