We developed a gesture salience based hand tracking method, and a gesture spotting and recognition method based on concatenated hidden Markov models. A 3-fold cross validation using the ChAirGest development data set with 10 users gives an F1 score of 0.907 and an accurate temporal segmentation rate (ATSR) of 0.923. The average final score is 0.9116. Compared with using the hand joint position from the Kinect SDK, using our hand tracking method gives a 3.7% absolute increase in the recognition F1 score.
With this data set, REF presented a hand tracking method based on gesture salience, and concatenated hidden Markov models were applied to perform gesture spotting and recognition.
Gesture spotting and recognition using salience detection and concatenated hidden markov models
ICMI '13
Computer Science