Human action recognition in video data using invariant characteristic vectors

We introduce the concept of the “characteristic vector” as an invariant vector associated with a set of freely moving points relative to a plane. We show that if the motion of two sets of points differ only up to a similarity transformation, then the elements of the characteristic vector differ up to scale regardless of viewing directions and cameras. Furthermore, this invariant vector is given by any arbitrary homography that is consistent with epipolar geometry. The characteristic vector of moving points can thus be used to recognize the transitions of a set of points in an articulated body during the course of an action regardless of the camera orientation and parameters. Our extensive experimental results on both motion capture data and real data indicates very good performance.