Trajectory Classification in n Dimensions using Subspace Projection

This paper presents a novel descriptor for trajectory classification in n dimensions, which is invariant with respect to scaling and rigid transformation. Using a hierarchical approach, the descriptor is able to capture both local and global features of the trajectory. The algorithm iteratively splits up every trajectory into smaller trajectory segments resulting in a binary tree. Inspired by the Frenet-Serret formulas, a projection onto a lower dimensional subspace is performed for every trajectory segment, providing a characteristic description of every trajectory. The subspace projection acts as a pseudo-curvature measure in every dimension. Successful applicability is shown through classification experiments in three and six dimensions using an RGB-D camera. For comparison with other algorithms, the Australian Sign Language dataset is also used for classification, showing a superior classification rate.