Bayesian Human Motion Intentionality Prediction in urban environments

Abstract

Human motion prediction in indoor and outdoor scenarios is a key issue towards human robot interaction and intelligent robot navigation in general. In the present work, we propose a new human motion intentionality indicator, denominated Bayesian Human Motion Intentionality Prediction (BHMIP), which is a geometric-based long-term predictor. Two variants of the Bayesian approach are proposed, the Sliding Window BHMIP and the Time Decay BHMIP. The main advantages of the proposed methods are: a simple formulation, easily scalable, portability to unknown environments with small learning effort, low computational complexity, and they outperform other state of the art approaches. The system only requires training to obtain the set of destinations, which are salient positions people normally walk to, that configure a scene. A comparison of the BHMIP is done with other well known methods for long-term prediction using the Edinburgh Informatics Forum pedestrian database and the Freiburg People Tracker database.