Incorporating Environment Models for Improving Vision-Based Tracking of People

摘要

This paper presents a method for real-time 3D human tracking based on the particle filter by incorporating environment models. We track a human head represented with its 3D position and orientation by integrating the multiple cues from a set of distributed sensors. In particular, the multi-viewpoint color and depth images obtained from distributed stereo camera systems and the 3D shape of an indoor environment measured with a range sensor are used as the cues for 3D human head tracking. The 3D shape of an indoor environment allows us to assume the existing probability of a human head (we call this probability the environment model). While tracking the human head, we consider the environment model to improve the robustness of tracking in addition to the multi-camera's color and depth images. These cues including the environment model are used in the hypothesis evaluation and integrated naturally into the particle filter framework. The effectiveness of our proposed method is verified through experiments in a real environment.