Improving trajectory classification through Kramers–Moyal coefficients

Abstract

Trajectory classification focuses on predicting the class or category of a moving object based on its observed movement over time. The classification of trajectory data using classical approaches can be challenging due to the arbitrary and relatively long length of some trajectories. To overcome this, trajectories are often mapped into vector representations that aim to encode their most significant features and for a fixed number of dimensions. Here we propose a novel vector representation for trajectories that combines previously employed features with new ones derived from the computation of the Kramers–Moyal coefficients (KMC). Due to KMC originating from a Taylor expansion that progressively encapsulates more information about a stochastic process, their potential to be effective in trajectory classification is a logical anticipation. We evaluated our representation using different classifiers and several benchmark datasets previously used for trajectory classification. With the addition of features extracted from KMCs, our results indicate a reliable increase in classification accuracy and F1 score of around 4% across all datasets and models used for evaluation. Moreover, we observed an increase in accuracy of up to 20% and an increase in F1 score of up to 23% in some scenarios.