Robust Online Trajectory Clustering without Computing Trajectory Distances

We propose a novel trajectory clustering algorithm which is suitable for online processing of pedestrian or vehicle trajectories computed with a vision-based tracker. Our approach does not require defining distances between trajectories, and can thus process broken trajectories which are inevitable in most cases when object trackers are applied to real world video footage. Clusters are defined as smooth vector fields on a bounded connected set, and cluster distance is based on pairwise distances between vector sets. The results are illustrated on a trajectory set from the Edinburgh Informatics Forum Pedestrian Dataset, on a trajectory set from a public transport junction, and trajectories from an experimental setup in a corridor.