Applications of HMM modeling to recognizing human gestures in image sequences for a man-machine interface

Efforts to understand human motion have been increasing in number and complexity, and will most likely prove to be a key component in human-computer interfaces. One key feature of motion in general, and human motion in particular, is its dynamic nature. The present work seeks to model human motions in a manner amenable to leaning and recognition. Toward this end, hidden Markov models (HMMs) are employed to model semantically meaningful human movements. The data used for modeling the human motions is an approximate pose derived from a sequence of camera images. An HMM is learned for each motion class and employed as a maximum likelihood recognizer. Experiments show promising results for a set of six sport actions.