A Quantitative Evaluation of Video-based 3D Person Tracking

The Bayesian estimation of 3D human motion from video sequences is quantitatively evaluated using synchronized, multi-camera, calibrated video and 3D ground truth poses acquired with a commercial motion capture system. While many methods for human pose estimation and tracking have been proposed, to date there has been no quantitative comparison. Our goal is to evaluate how different design choices influence tracking performance. Toward that end, we independently implemented two fairly standard Bayesian person trackers using two variants of particle filtering and propose an evaluation measure appropriate for assessing the quality of probabilistic tracking methods. In the Bayesian framework we compare various image likelihood functions and prior models of human motion that have been proposed in the literature. Our results suggest that in constrained laboratory environments, current methods perform quite well. Multiple cameras and background subtraction, however, are required to achieve reliable tracking suggesting that many current methods may be inappropriate in more natural settings. We discuss the implications of the study and the directions for future research that it entails