  This paper presents a pedestrian motion model that includes both low level
trajectory patterns, and high level discrete transitions. The inclusion of both
levels creates a more general predictive model, allowing for more meaningful
prediction and reasoning about pedestrian trajectories, as compared to the
current state of the art. The model uses an iterative clustering algorithm with
(1) Dirichlet Process Gaussian Processes to cluster trajectories into
continuous motion patterns and (2) hypothesis testing to identify discrete
transitions in the data called transition points. The model iteratively splits
full trajectories into sub-trajectory clusters based on transition points,
where pedestrians make discrete decisions. State transition probabilities are
then learned over the transition points and trajectory clusters. The model is
for online prediction of motions, and detection of anomalous trajectories. The
proposed model is validated on the Duke MTMC dataset to demonstrate
identification of low level trajectory clusters and high level transitions, and
the ability to predict pedestrian motion and detect anomalies online with high
accuracy.
