Selecting the right features for the representation of human motion dynamics is the first step towards developing a recognition paradigm. We cannot just use intensity or color trajectories as they are not representative of the true motion. In the past, researchers have used motion capture data, however accurately extracting limbs and joints from a video without using stick-up markers is difficult. Moreover, there is always the question whether the representation should be unique to the whole person i.e. global, or whether a collection of local representations such as a parts-based approach is better. Global motion models allow for the construction of a spatially compact and temporally consistent formulation. However the drawback is that these models break down when there is a large amount of occlusion of the subject, in which case parts-based or local approaches are applicable. Local models however do not take into account of the dynamics of the scene. Since our goal is to explicitly model the dynamics of human motion, we propose a global model.

Assuming that there is only one person moving in the scene and the background is stationary, we first compute the optical flow for each frame of the entire video. We quantize the optical flow as shown in figure 2. Each optical flow vector is binned according to its principal angle with the horizontal. The contribution to each bin is wiehted by the magnitude of the optical flow vector. This gives us a scale and fronto-parallel direction invariant feature, a Histogram of Oriented Optical Flow (HOOF) in each frame. The time series of such HOOF represents the motion in the scene.