  This paper presents a novel incremental learning algorithm for pedestrian
motion prediction, with the ability to improve the learned model over time when
data is incrementally available. In this setup, trajectories are modeled as
simple segments called motion primitives. Transitions between motion primitives
are modeled as Gaussian Processes. When new data is available, the motion
primitives learned from the new data are compared with the previous ones by
measuring the inner product of the motion primitive vectors. Similar motion
primitives and transitions are fused and novel motion primitives are added to
capture newly observed behaviors. The proposed approach is tested and compared
with other baselines in intersection scenarios where the data is incrementally
available either from a single intersection or from multiple intersections with
different geometries. In both cases, our method incrementally learns motion
patterns and outperforms the offline learning approach in terms of prediction
errors. The results also show that the model size in our algorithm grows at a
much lower rate than standard incremental learning, where newly learned motion
primitives and transitions are simply accumulated over time.
