Flexible signature descriptions for adaptive motion trajectory representation, perception and recognition
Motion trajectory is a meaningful and informative clue in characterizing the motions of human, robots or moving objects. Hence, it is important to explore effective motion trajectory modeling. However, with the existing methods, a motion trajectory is used in its raw data form and effective trajectory description is lacking. In this paper, we propose a novel 3D motion trajectory signature descriptor and develop three signature descriptions for motion characterization. The flexible descriptions give the signature high functional adaptability to meet various application requirements in trajectory representation, perception and recognition. The full signature, optimized signature and cluster signature are firstly defined for trajectory representation. Then we explore the motion perception from a single signature, inter-signature matching and the generalization of a cluster signature. Furthermore, three solutions for signature recognition are investigated corresponding to different signature descriptions. The conducted experiments verified the signature's capabilities and flexibility. The signature's application to robot learning is also discussed.