Scaling it up

[jax79sg]

Hi, i am trying to do a proof of concept showing how Neural Network trackers could potentially be used in practical day to day scenarios.

Correct me if i am wrong, the current implementation is such that each initialized model can only track one object at a single time. This takes a few hundred of megabytes of memory off the GPU. If i were to concurrently track X number of distinct objects in the same video sequence, i would essentially need to initialize the same number of models which would most likely max out the GPU RAM. Is there a way to get around this?

Thanks.