Visual tracking of independently moving body and arms

Tracking of the upper human body is one of the most interesting and challenging research fields in computer vision and comprises an important component used in gesture recognition applications. In this paper a probabilistic approach towards arm and hand tracking is presented. We propose the use of a kinematics model together with a segmentation of the parameter space to cope with the space dimensionality problem. Moreover, the combination of particle filters with hidden Markov models enables the simultaneous tracking of several hypotheses for the body orientation and the configuration of each of the arms.