Trajectory kinematics descriptor for trajectory clustering in surveillance videos

Trajectories provide spatial-temporal information of foreground objects for event clustering and analysis. Because of the kinematic properties of foreground objects, the lengths of trajectories will be different which lead to the length problem of assessing similarity between two or more trajectories. To solve the problem, we propose a novel descriptor named trajectory kinematics descriptor to represent trajectories based on the kinematic properties from the point-of-view of Frenet-Serret frames. As shown in the experiments, applying the proposed trajectory kinematics descriptor for event clustering can achieve better F-measure scores compared to the state-of-the-art methods.