Learning Stochastic Path Planning Models from Video Images

We describe a probabilistic framework for learning models of pedestrian trajectories in general outdoor scenes. Possible applications include simulation of motion in computer graphics, video surveillance, and architectural design and analysis. The models are based on a combination of Kalman filters and stochastic path-planning via landmarks, where the landmarks are learned from the data. A dynamic Bayesian network (DBN) framework is used to represent the model as a position-dependent switching state space model. We illustrate how such models can be learned and used for prediction using the block Gibbs sampler with forward-backward recursions. The ideas are illustrated using a real world data set collected in an unconstrained outdoor scene.