Human activity recognition from frame’s spatiotemporal representation

This paper presents an approach for human activity recognition by representing the frames of the video sequence with the distribution of local motion features and their spatiotemporal arrangements. In this approach, the local motion features used for the representation of a frame are integrated from the ones detected in this frame and its temporal neighbors. The featurespsila spatial arrangements are captured in a hierarchical spatial pyramid structure. By using frame by frame voting for the recognition, experiments have demonstrated improved performances over most of the other known methods on the popular benchmark data sets while approaching the best known results.