Orientation histogram of SIFT displacement for recognizing actions in broadcast videos

Human actions are diversified and complicated, and hence difficult to be recognized by artificial intelligence systems. Recognizing actions in broadcast videos is even more challenging due to low resolution and frame rate. During the past decades, many talented researchers are devoted to this field, and several promising algorithms are developed to achieve great performance. The well known methods include optical flow, 3D Harris, Cuboid detector, 3D SIFT, to name a few. Inspired by preceding algorithms, we propose using SIFT displacement between consecutive frames to extract human motions in broadcast videos. Motion descriptors are orientation histogram computed from displacement vectors voted by moving distances. Geometric information is also employed by dividing frames into sub-regions. Experimental results indicate that the proposed approach is effective and outperforms other state-of-the-art methods.