Goal-Based Person Tracking Using a First-Order Probabilistic Model

This work addresses the problem of person tracking using additional background information. We augment a particle filter-based tracking algorithm with a first-order probabilistic model expressed through Markov Logic Networks to tackle the data association problem in domains with a high occlusion rate. Using a high-level model description allows us to easily integrate additional information like a floor plan or goal information into a joint model and resolve occlusion situations that would otherwise result in the loss of association. We discuss the engineered model in detail and give an empirical evaluation using an indoor setting.