Segmentation and recognition of multi-attribute motion sequences

Abstract

In this work, we focus on fast and efficient recognition of motions in multi-attribute continuous motion sequences. 3D motion capture data, animation motion data, and sensor data from gesture sensing devices are examples of multi-attribute continuous motion sequences. These sequences have multiple attributes rather than only one attribute as time series data has. Motions can have different rates and durations, and the resulting data can thus have different lengths. Also, motion data can have noises due to transitions between successive motions. Hence, traditional distance measuring approaches used for time series data (such as Euclidean distances or dynamic time-warped distances) are not suitable for recognition in multi-attribute motion sequences. Hence, we have defined a similarity measure based on the analysis of singular value decomposition (SVD) properties of similar multi-attribute motions. A five-phase algorithm has then been proposed that gives good pruning power by exploiting the proximity of continuous motion data. We experimented this algorithm with data from different sources: 3D motion capture devices, animation motions, and CyberGlove gesture sensing device. These experiments show that our algorithm can segment and recognize long motion streams with high accuracy and in real time without knowing beforehand the number of motions in a stream.