A hierarchical motion trajectory signature descriptor

Motion trajectory is a compact clue for motion characterization. However, it is normally used directly in its raw data form in most work and effective trajectory description is lacking. In this paper, we propose a novel hierarchical motion trajectory signature descriptor, which can not only fully capture motion features for detailed perception, but also can be used for probabilistic fast recognition. The hierarchy enables the signature to exhibit high functional adaptability meeting different application requirements. At the first-level, differential invariants are employed to describe trajectory features and a nonlinear signature warping method is developed to perceive and recognize trajectories. The second-level signature is the condensation of the first-level signature by applying PCA based dimension optimization. It behaves more efficiently in recognition based on the Gaussian Mixture modeling and Bayesian classifier. The conducted experiments verified the signature´s effectiveness.