Variable structure multiple model for articulated human motion tracking from monocular video sequences
A new model-based human body tracking framework with learning-based theory is introduced in this paper. We propose a variable structure multiple model (VSMM) framework to address challenging problems such as uncertainty of motion styles, imprecise detection of feature points, and ambiguity of joint locations. Key human joint points are detected automatically and the undetected points are estimated with Kalman filters. Multiple motion models are learned from motion capture data using a ridge regression method. The model set that covers the total motion set is designed on the basis of topological and compatibility relationships, while the VSMM algorithm is used to estimate quaternion vectors of joint rotation. Experiments using real image sequences and simulation videos demonstrate the high efficiency of our proposed human tracking framework.
human motion tracking variable structure multiple model (vsmm) ridge regression model groups design motion model group adaptation