Track-Before-Detect Labeled Multi-Bernoulli Smoothing for Multiple Extended Objects

For the evaluation of autonomous driving systems, this paper provides a new approach of generating reference data for multiple extended object tracking. In our approach, we apply a forward-backward smoother for objects with star-convex shapes based on the Labeled Multi-Bernoulli (LMB) Random Finite Set (RFS) and recursive Gaussian processes. We further propose to combine a robust birth policy with a backward filter to solve the conflict between robustness and completeness of tracking. Thereby, cluster candidates are evaluated based on a quality measure to only initialize objects from more reliable clusters in the forward pass. Missing states will then be recovered by the backward filter through post-processing the unassociated data after the smoothing process. Simulations and real-world experiments demonstrate superior performance of the proposed method in both cardinality and individual state estimation compared to naive LMB filter and smoother for extended objects.