Most robotic companies today still use traditional tracking and filtering (e.g. kalman filters) to help with associating detected objects with tracks (objects over time). Solving this in an fully differentiable / ML-first way for multiple targets is still WIP at most companies, since deepnet-to-detect + filtering is still a strong baseline and there are still challenges to be solved.

Occlusions, short-lived tracks, misassociations, low frame rate + high-rate-of-change features (e.g. flashing lights) are all still very challenging when you get down to brass tacks.