[nutanc]

I would do the following:

- Manually scan through a couple of hours of data and setup a human baseline.

- Run standard algorithms and find their accuracy.

- Find errors in the model and analyze why the errors are happening. Is the model classifying some other object as a supervisor? Is the model not classifying the supervisor in certain lighting conditions or scenarios.

- Retrain the model with the failure scenarios so that it learns.