Multi-feature trajectory clustering using Earth Mover's Distance

We present new results in trajectory clustering, obtained by extending a recent methodology based on Earth Mover's Distance (EMD). The EMD can be adapted as a tool for trajectory clustering, taking advantage of an effective method for identifying the clusters' representatives by means of the p-median location problem. This methodology can be used either in an unsupervised fashion, or on-line, classifying new trajectories or part of them; it is able to manage different length and noisy trajectories, occlusions and takes velocity profiles and stops into account. We extend our previous work by taking into account other features besides the spatial locations, in particular we consider the direction of movement in correspondence of each trajectory point. We discuss the simulation results and we compare our approach with another trajectory clustering method.