Jacobian images of super-resolved texture maps for model-based motion estimation and tracking

We present a Kalman filter based approach to perform model-based motion estimation and tracking. Unlike previous approaches, the tracking process is not formulated as an SSD minimization problem, but is da eloped by using texture mapping as the measurement model in an extended Kalman filter. During tracking, a super-resolved estimate of the texture present on the object or in the scene is obtained. A key result is the notion of Jacobian images, which can be viewed as a generalization of traditional gradient images, and represent the crucial computation in the tracking process. The approach is illustrated with three sample applications: full 3D tracking of planar surface patches, a projective surface tracker for uncalibrated camera scenarios, and a fast, Kalman filtered version of mosaicking with detection of independently moving objects.