Human motion prediction considering environmental context
Igi Ardiyanto, Jun Miura · 2015
This paper describes an approach to predict the human motion. Instead of using a simple motion model as widely used, we take advantages of the environmental context, including the shape and structure, for predicting the human movement. First, we characterize the environment using a graph representation. Subsequently, we acquire the human trajectory tendency on each environment and build a probabilistic sequence model of the human motion. A particle filter-based predictor is then integrated into the system for generating possible future paths of the person. Evaluations on a real campus environment show the advantages of the proposed approach.