Point Context: An Effective Shape Descriptor for RST-Invariant Trajectory Recognition

Motion trajectory recognition is important for characterizing the moving property of an object. The speed and accuracy of trajectory recognition rely on a compact and discriminative feature representation, and the situations of varying rotation, scaling, and translation have to be specially considered. In this paper, we propose a novel feature extraction method for trajectories. Firstly, a trajectory is represented by a proposed point context, which is a rotation-scale-translation invariant shape descriptor with a flexible tradeoff between the complexity and discrimination, yet we prove that it is a complete shape descriptor. Secondly, the point context is nonlinearly mapped to a subspace by kernel nonparametric discriminant analysis to get a compact feature representation, and thus a trajectory is projected to a low-dimensional feature space. Experimental results show that the proposed trajectory feature demonstrates encouraging improvement than state-of-the-art methods.