Action recognition based on homography constraints

In this paper, we present a new approach for view-invariant action recognition using constraints derived from the eigenvalues of planar homographies associated with triplets of body points. Unlike existing methods that study an action as a whole, or break it down into individual poses, we represent an action as a sequence of pose transitions. Using the fact that the homography induced by the motion of a triplet of body points in two identical pose transitions reduces to the special case of a homology, we exploit the equality of two of its eigenvalues to impose constraints on the similarity of the pose transitions between two subjects, observed by different perspective cameras and from different viewpoints. Experimental results show that our method can accurately identify human pose transitions and actions even when they include dynamic timeline maps, and are obtained from totally different viewpoints with different camera parameters.