If you look closely at DeepSORT, you'll find that the topics used in OP's repo are fundamental pieces of its implementation.

And with that in mind, things like DeepSORT are generally computer-vision specific, while the stuff in OP's post is more general and applies to more sensor/detection sources.

There are many applications of tracking that don't involve an optical sensor source.

Actually, DeepSORT doesn't use a hypothesis tree, which is common in many hyped CV tracking algorithms. I believe this is due to the abundant contextual data available in dense images. However, in self-driving scenarios, where other sensors are involved and the data (like sparse point clouds) is more challenging, these algorithms might not be as effective. The tracking requirements in such cases, often dealing with small and sparsely distributed data and frequent missed detections, necessitate improved and different tracking approaches.