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Detecting Human Action in Active Video

We propose a novel scheme to detect human actions in active video. Active videos such as movies or sports broadcasting are taken purposively by "clever" photographers. They are object and action oriented and usually involve complex camera motions. Detecting actions in active videos is both important and challenging. We study a three-step scheme to detect complex human actions in such videos. The proposed method first locates potential objects and removes clutter with a composite filter scheme. The detected object candidates in successive frames are then associated to form object trajectories based on a consistent labeling formulation, and solved with belief propagation. Finally, specific human actions are detected in video with a linear programming matching approach that can efficiently deal with matching problems having a large target point set. The proposed method has been successfully applied in action detection for general videos and TV hockey games