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

Motion trajectory contains plentiful motion information of moving objects, for example, human gestures and robot actions. Motion perception and recognition via trajectory are useful for characterizing them and a flexible descriptor of motion trajectory plays important role in motion analysis. However, in the existing tasks, trajectories were mostly used in raw data and effective descriptor is lacking. In this paper, we present a mixed invariant signature descriptor with global invariants for motion perception and recognition. The mixed signature is viewpoint invariant for local and global features. A reliable approximation of the mixed signature is proposed to reduce the noise in high-order derivatives. We use this descriptor for motion trajectory description and explore the motion perception with DTW algorithm for salient motion features. To achieve better accuracy, we modified the CDTW algorithm for trajectory matching in motion recognition. Furthermore, a controllable weight parameter is introduced to adjust the global features for tasks in different circumstances. The conducted experiments validated the proposed method.