Robust Spotting of Key Gestures from Whole Body Motion Sequence

Robust gesture recognition in video requires segmentation of the meaningful gestures from a whole body gesture sequence. This is a challenging problem because it is not straightforward to describe and model meaningless gesture patterns. This paper presents a new method for simultaneous spotting and recognition of whole body key gestures. A human subject is first described by a set of features encoding the angular relations between a dozen body parts in 3D. A feature vector is then mapped to a codeword of gesture HMMs. In order to spot key gestures accurately, a sophisticated method of designing a garbage gesture model is proposed; a model reduction which merges similar states based on data-dependent statistics and relative entropy. This model provides an effective mechanism for qualifying or disqualifying gestural motions. The proposed method has been tested with 20 persons´ samples and 80 synthetic data. The proposed method achieved a reliability rate of 94.8% in spotting task and a recognition rate of 97.4% from an isolated gesture