Statistical motion characterization for video content classification

This work proposed using a unified model to characterize the motion variations along both the spatial and temporal domains. To this end, we estimate the motion quantities from the pixelwise normal flow and represent the motion distribution using two Gibbs models: temporal and spatial Gibbs models. We measure the potential values of the two Gibbs models by the maximum likelihood criterion. To demonstrate the effectiveness of the proposed model, we have applied the motion model for the application of video content classification. Experimental results show that using the proposed model indeed improves the classification performance.