Articulated human motion tracking with foreground learning
Résumé
Tracking the articulated human body is a challenging computer vision problem because of changes in body poses and their appearance. Pictorial structure (PS) models are widely used in 2D human pose estimation. In this work, we extend the PS models for robust 3D pose estimation, which includes two stages: multi-view human body parts detection by foreground learning and pose states updating by annealed particle filter (APF) and detection. Moreover, the image dataset F-PARSE was built for foreground training and flexible mixture of parts (FMP) model was used for foreground learning. Experimental results demonstrate the effectiveness of our foreground learning-based method.