Track-to-track Association based on Deterministic Sampling using Herding

Multi-sensor multi-object tracking in a track-to-track fusion framework involves the grouping of tracks (from different sensors) that belong to the same perceived object. In particular for collective perception scenarios in large-scale traffic systems the number of sensors and objects can be huge, as a large number of vehicles can be equipped with multiple sensors. In order to cope with the intractable number of possible associations, recently a stochastic optimization approach for track-to-track association was proposed. The key idea is to successively improve an initial association by means of performing random modifications, i.e., actions, on the current association. In this work, we develop a novel deterministic version of the algorithm, which employs herding in order to deterministically choose the next action. Simulations demonstrate that the deterministic version of stochastic optimization provides comparable results to the stochastic version with a significantly lower variance.