So squeezing the training data to match animorphic 16:9 video didn't give any hits.  When the test video was cropped to 1:1 again, hits bounced back to the same as if the training data was never squeezed.  It somehow knew the test video was cropped instead of stretched without any insight from the training data.  It is believed animorphic video squeezes the details below the minimum resolution, hence why it fails to track lions facing sideways.

The best option would now be changing the input layer size, but the internet only says not to attempt this.  1 problem is expanding the input layer causes an exponential increase in computations.

 Another option could be tiling 2 widened images in the input layer.  That would drop the vertical resolution to 160 while increasing the horizontal resolution to 640.  It would cause a blind spot in the middle.

The leading idea is panning the 1:1 frame inside the 16:9 frame to follow the hit.  It sweeps back & forth when it has no hit.

Object detection has always been dependent on aspect ratio.  Openpose only worked with 16:9 video but fell over on 1:1 video.  It was always assumed to be the training data being stretched to match the test video.