Head pose based intention prediction using Discrete Dynamic Bayesian Network
Intention prediction is an active research topic, however, few work make use of the head pose. This research investigates the usefulness of head pose and pose change features into a Discrete Dynamic Bayesian Network to predict pedestrian's intention. In particular, we first focus on scenarios within a shopping center (using the standard CAVIAR database), to predict whether a pedestrian will enter a shop or not. We assume that different head pose patterns reflect different intentions. Thus, a hierarchical clustering method is used to process the histogram of head pose and the change of head pose. Then the clustering results are used as features feeding into a dynamic bayesian network. Experiments have been conducted to demonstrate the importance of using head pose in intention prediction and it shows a fairly good result which use only the information derived from pedestrian themselves regardless of the complex environment. The proposed approach is simple to use and makes it easy to integrate other features.