Double-feature Combination Based Approach to Motion Capture Data Behavior Segmentation
Xin Liu · 2013
The objective of motion capture data behavior segmentation is to divide the original long motion sequence into several motion fragments,and each motion fragment incorporates a particular semantic behavior.In general,the transition parts of some neighboring motion fragments are always encountered with the semantic ambiguity.To this end,this paper presented a double-feature combination based approach to tackle this problem.The proposed approach first extracts two different types of motion features,i.e.,angle,distance,and then utilizes the PPCA algorithm to construct two different comprehensive characteristic functions individually.Subsequently,a subinterval standard deviation approach associated with threshold limiting strategy is employed to segment the comprehensive characteristic functions into several confidence regions and pending regions roughly.Finally,by utilizing the Gaussian mixture model to further determine the pending regions,the robust segmentation result can be obtained.The experimental results show that the proposed approach performs favorably compared to the state-of-the-art methods.