Issue No. 06 - June (2011 vol. 33)
Bo Peng , Arizona State University, Tempe
Gang Qian , Arizona State University, Tempe
This paper presents a robust framework for online full-body gesture spotting from visual hull data. Using view-invariant pose features as observations, hidden Markov models (HMMs) are trained for gesture spotting from continuous movement data streams. Two major contributions of this paper are 1) view-invariant pose feature extraction from visual hulls, and 2) a systematic approach to automatically detecting and modeling specific nongesture movement patterns and using their HMMs for outlier rejection in gesture spotting. The experimental results have shown the view-invariance property of the proposed pose features for both training poses and new poses unseen in training, as well as the efficacy of using specific nongesture models for outlier rejection. Using the IXMAS gesture data set, the proposed framework has been extensively tested and the gesture spotting results are superior to those reported on the same data set obtained using existing state-of-the-art gesture spotting methods.
Online gesture spotting, view invariance, multilinear analysis, visual hull, hidden Markov models, nongesture models.
G. Qian and B. Peng, "Online Gesture Spotting from Visual Hull Data," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 1175-1188, 2010.