Action Recognition Using Direction Models of Motion

In this paper, we present an effective method for human action recognition using statistical models based on optical flow orientations. We compute a distribution mixture over motion orientations at each spatial location of the video sequence. The set of estimated distributions constitutes the direction model, which is used as a mid-level feature for the video sequence. We recognize human actions using a distance metric to compare the direction model of a query sequence with the direction models of training sequences. The experimentations have been performed on standard datasets and have showed promising results.