Head tracking algorithms based on elliptical deformable templates
Da Jia · Jisuanji gongcheng yu sheji · 2009
A new head-tracking algorithm based on deformable models, optical flow and extended Kalman filter techniques is proposed, which combined the shapes, motion and color cues of objects in images in order to acquire a fast and robust scheme of head tracking. The algorithm used ellipse as a deformable templates to capture the edge of objects. With no needs of any special smoothness constraint, the optical flow is estimated by model-based optical flow method in YCrCb color space. By using of the extended Kalman filter, the measurements of shape and motion are naturally integrated to provide an effective fusion solution. An optical-flow based measurement error and the estimated covariance given by the EKF filter are used to detect and reject the contour sample points that correspond to noise, occlusions or spurious edge. Experiments results are presented to validate the algorithms.