Histograms of Motion Gradients for real-time video classification
Besides appearance information, the video contains temporal evolution, which represents an important and useful source of information about its content. Many video representation approaches are based on the motion information within the video. The common approach to extract the motion information is to compute the optical flow from the vertical and the horizontal temporal evolution of two consecutive frames. However, the computation of optical flow is very demanding in terms of computational cost, in many cases being the most significant processing step within the overall pipeline of the target video analysis application. In this work we propose a very efficient approach to capture the motion information within the video. Our method is based on a simple temporal and spatial derivation, which captures the changes between two consecutive frames. The proposed descriptor, Histograms of Motion Gradients (HMG), is validated on the UCF50 human action recognition dataset. Our HMG pipeline with several additional speed-ups is able to achieve real-time video processing and outperforms several well-known descriptors including descriptors based on the costly optical flow.