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In particular, a global overview of the multiple pedestrian motion models is given, and the pedestrian image detector is described.
Description of the earthquake acceleration time history through stochastic ground motion models is considered in this context.
Trajectory prediction with Maneuver-based motion models is based on the early recognition of the maneuvers that drivers intend to perform.
A multiple model particle filter, which uses two different motion models, is designed for enabling the tracking of both road-constrained (on-road) and unconstrained (off-road) targets.
After the two motion modeling branches have provided their best estimations, a motion model decision (step 11) that selects between the perspective and translational motion models is performed along with the final motion compensation and warping to fuse the available SI estimations (step 12).
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The Interaction-aware motion models are the most comprehensive models proposed so far in the literature.
Maneuver-based motion models are either based on prototype trajectories or based on maneuver intention estimation.
Finally, the limitations of Maneuver-based motion models are analyzed in Section 'Limitations'.
Most Interaction-aware motion models are based on Dynamic Bayesian Networks (DBN).
As a result, higher-order motion models are seldom used in practical coding applications.
Finally, the limitations of Physics-based motion models are addressed in Section 'Limitations'.
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