Trajectory clustering for motion pattern extraction in aerial videos

Abstract

We present an end-to-end approach for trajectory clustering from aerial videos that enables the extraction of motion patterns in urban scenes. Camera motion is first compensated by mapping object trajectories on a reference plane. Then clustering is performed based on statistics from the Discrete Wavelet Transform coefficients extracted from the trajectories. Finally, motion patterns are identified by distance minimization from the centroids of the trajectory clusters. The experimental validation on four datasets shows the effectiveness of the proposed approach in extracting trajectory clusters. We also make available two new real-world aerial video datasets together with the estimated object trajectories and ground-truth cluster labeling.