Head rotation classification using dense motion estimation and particle filter tracking

We propose a method that performs dense motion classification integrated with particle filter tracking for monitoring whether the viewer is involved in the screened content or not. We first perform the color based particle filtering that enables us tracking head of the user through the video sequence. It is followed by optical flow estimation via SIFT flow applied on the tracked regions. Finally the features extracted based on the viewer head rotation and location are fed into the random forest classifier to report the involvement level of the tracked person. It is shown that the used probabilistic motion estimation model with the support of tracking significantly reduces the computational complexity while it provides comparable performance with the state-of-the-art methods. The proposed scheme allows online monitoring the viewer therefore can be integrated to the interactive multimedia systems.