Better Motion Prediction for People-tracking

An important building block for intelligent mobile robots is the ability to track people moving around in the environment. Algorithms for person-tracking often incorporate motion models, which can improve tracking accuracy by predicting how people will move. More accurate motion models produce better tracking because they allow us to average together multiple predictions of the person’s location rather than depending entirely on the most recent observation. Many implemented systems, however, use simple conservative motion models such as Brownian motion (in which the person’s direction of motion is independent on each time step). We present an improved motion model based on the intuition that people tend to follow efficient trajectories through their environments rather than random paths. Our motion model learns common destinations within the environment by clustering training examples of actual trajectories, then uses a path planner to predict how a person would move along routes from his or her present location to these destinations. We have integrated this motion model into a particle-filter-based person-tracker, and we demonstrate experimentally that our new motion model performs significantly better than simpler models, especially in situations in which there are extended periods of occlusion during tracking.