Optical-flow features empirical mode decomposition for motion anomaly detection

In video data analysis of dynamic scenes, temporal characteristics of moving objects play an important role in decision-making. However, the temporal consistency of typical features used for video interpretation is low due to the overlap of the spectra of informative video signal component and the stochastic variations perturbing it. We propose a novel method for object motion anomaly detection in video designed to overcome this problem. It is based on empirical mode decomposition. We show in experiments on a benchmarking dataset that the deterministic component of an optical flow feature obtained using the proposed method is able to isolate the periodic behaviour of the motion from the stochastic values, facilitating much simpler analysis of the motion patterns and achieving impressive anomaly detection performance.