Latent Linear Dynamics for Modeling Pedestrian Behaviors

摘要

We consider the problem of generative pedestrian modeling for capturing common behaviors constituting trajectory dataset. We present a model, that simultaneously represents the trajectory data and latent dynamics associated with different behaviors. Model represents the trajectory dynamics as scaled component of cluster dynamics, and overall the cluster dynamics is shared among all trajectories belonging to a cluster, thus giving rise to similarity. Cluster dynamics is modeled by incorporating Bayesian nonparametrics, particularly the usage of Dirichlet process mixture model approach, which relaxes the number of unique behaviors or clusters. Additionally, the relative velocity scaling term encapsulates the relative nature of an individual trajectory to its cluster dynamics. Model parameters and latent states are inferred using sequential blocked Gibbs sampler, which can be scaled to large datasets.