Simultaneous Tracking and Activity Recognition with Relational Dynamic Bayesian Networks

Abstract

Taking into account relationships between interacting objects can improve the understanding of the dynamic model governing their behaviors. Moreover, maintaining a belief about the ongoing activity while tracking allows online activity recognition and improves the tracking task. We investigate the use of Relational Dynamic Bayesian Networks to represent the relationships for the tasks of multi-target tracking and explicitly consider a discrete variable in the state to represent the activity for online activity recognition. We propose a new transition model that accommodates relations and activities and we extend the Particle Filter algorithm to directly track relations between targets while recognizing ongoing activities.