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Motion trajectory contains plentiful motion information of moving objects, for example, human gestures and robot actions. Motion perception and recognition via trajectory areuseful for characterizing them and a flexible descriptor of motion trajectory plays importantrole in motion analysis. However, in the existing tasks, trajectories were mostly used in rawdata and effective descriptor is lacking. In this paper, we present a mixed invariant signaturedescriptor with global invariants for motion perception and recognition. The mixed signatureis viewpoint invariant for local and global features. A reliable approximation of the mixedsignature is proposed to reduce the noise in high-order derivatives. We use this descriptor formotion trajectory description and explore the motion perception with DTW algorithm forsalient motion features. To achieve better accuracy, we modified the CDTW algorithm fortrajectory matching in motion recognition. Furthermore, a controllable weight parameter isintroduced to adjust the global features for tasks in different circumstances. The conductedexperiments validated the proposed method.

Mixed Signature: An Invariant Descriptor for 3D Motion Trajectory Perception and Recognition