[ssivark]

Awesome summary. Welcome to some lessons/truths (circa 2019 state of technology):

1. Deep learning (by itself) is often a shitty solution. It takes a lot of fiddling with not just the models, but also the training data — to get anything useful. Often the data generation team/effort becomes larger than the model-building effort.

2. It is hopeless to use neural networks as an end-to-end solution. This example will involve studying whether detections are correlated/independent in neighboring frames... whether information can be pooled across frames... whether you can use that to build a robust real-time of the scene of interest, etc. That will involve lots of judicious software system design using broader ideas from ML / statistical reasoning.

This is why I find it hopelessly misleading to tell people to just find tutorials with TensorFlow/Pytorch and get started. You really need to understand what’s going on to be able to build useful systems.

That’s apart from all the thorny ethical questions raised by monitoring humans.