Probabilistic Cluster Signature for Modeling Motion Classes

In this paper, a novel 3-D motion trajectory signature is introduced to serve as an effective description to the raw trajectory. More importantly, based on the trajectory signature, a probabilistic model-based cluster signature is further developed for modeling a motion class. The cluster signature is a mixture model-based motion description that is useful for motion class perception, recognition and to benefit a generalized robot task representation. The signature modeling process is supported by integrating the EM and IPRA algorithms. The conducted experiments verified the cluster signature´s effectiveness.