Incorporating Long-Term Observations of Human Actions for Stable 3D People Tracking

Abstract

We propose a method for enhancing the stability of tracking people by incorporating long-term observations of human actions in a scene. Basic human actions, such as walking or standing still, are frequently observed at particular locations in an observation scene. By observing human actions for a long period of time, we can identify regions that are more likely to be occupied by a person. These regions have a high probability of a person existing compared with others. The key idea of our approach is to incorporate this probability as a bias in generating samples under the framework of a particle filter for tracking people. We call this bias the environmental existence map (EEM). The EEM is iteratively updated at every frame by using the tracking results from our tracker, which leads to more stable tracking of people. Our experimental results demonstrate the effectiveness of our method.