Markerless human motion tracking with a flexible model and appearance learning

Abstract

A new approach to the 3D human motion tracking problem is proposed, which combines several particle filters with a physical simulation of a flexible body model. The flexible body model allows the partitioning of the state space of the human model into much smaller subsets, while finding a solution considering all the partial results of the particle filters. The flexible model also creates the necessary interaction between the different particle filters and allows effective semi-hierarchical tracking of the human body. The physical simulation does not require inverse kinematics calculations and is hence fast and easy to implement. Furthermore the system also builds an appearance model on-the-fly which allows it to work without a foreground segmentation. The system is able to start tracking automatically with a convenient initialization procedure. The implementation runs with 10 Hz on a regular PC using a stereo camera and is hence suitable for Human-Robot Interaction applications.