TrackOpt

The goal of this project is to create an efficient method framework that enables new tracking problems to be flexibly integrated and reliably solved. Model-driven, potential discrete optimization problems are integrated with deep neural networks so that the strengths of both approaches can be exploited. In particular, the integration of model-driven knowledge means that the planned framework can work in a very data-efficient manner. At the same time, learned networks are optimized to predict consistent solutions despite uncertainty and measurement inaccuracy.