A similarity measure for motion stream segmentation and recognition

Recognition of motion streams such as data streams generated by different sign languages or various captured human body motions requires a high performance similarity measure. The motion streams have multiple attributes, and motion patterns in the streams can have different lengths from those of isolated motion patterns and different attributes can have different temporal shifts and variations. To address these issues, this paper proposes a similarity measure based on singular value decomposition (SVD) of motion matrices. Eigenvector differences weighed by the corresponding eigenvalues are considered for the proposed similarity measure. Experiments with general hand gestures and human motion streams show that the proposed similarity measure gives good performance for recognizing motion patterns in the motion streams in real time.