Point Context: An Effective Shape Descriptor for RST-invariant Trajectory Recognition
This addresses the need for robust trajectory recognition in applications like surveillance or robotics, though it appears incremental as it builds on existing shape context and feature extraction techniques.
The paper tackles the problem of motion trajectory recognition under varying rotation, scaling, and translation by proposing a novel RST-invariant shape descriptor called point context, which achieves encouraging improvements over state-of-the-art methods in experiments.
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 has 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 (RST) invariant shape descriptor with a flexible tradeoff between computational complexity and discrimination, yet we prove that it is a complete shape descriptor. Secondly, the shape context is nonlinearly mapped to a subspace by kernel nonparametric discriminant analysis (KNDA) to get a compact feature representation, and thus a trajectory is projected to a single point in a low-dimensional feature space. Experimental results show that, the proposed trajectory feature shows encouraging improvement than state-of-art methods.