Utilizing Learned Motion Patterns to Robustly Track Persons

Whenever people move through their environments they do not move randomly. Instead, they usually follow specific trajectories or motion patterns corresponding to their intentions. Knowledge about such patterns may enable a mobile robot to robustly keep track of the position of the persons in its environment or to improve its behavior. This paper proposes a technique for learning collections of trajectories that characterize typical motion patterns of persons. Data recorded with laser-range finders is clustered using the expectation maximization algorithm. Based on the result of the clustering process we derive a Hidden Markov Model (HMM). This HMM is able to estimate the current and future positions of multiple persons given knowledge about their typical motion patterns. Experimental results obtained with a mobile robot using laser and vision data collected in a typical office building with several persons illustrate the reliability and robustness of the approach. We also demonstrate that our model provides better estimates than an HMM directly learned from the data.