Optical flow estimation based on the structure-texture image decomposition

Optical flow approaches for motion estimation calculate vector fields which determine the apparent velocities of objects in time-varying image sequences. Image motion estimation is a fundamental issue in low-level vision and is used in many applications in image sequence processing, such as robot navigation, object tracking, image coding and structure reconstruction. The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. Actually, several methods are used to estimate the optical flow, but a good compromise between computational cost and accuracy is hard to achieve. This work presents a combined local-global total variation approach with structure-texture image decomposition. The combination is used to control the propagation phenomena and to gain robustness against illumination changes, influence of noise on the results and sensitivity to outliers. The resulted method is able to compute larger displacements in a reasonable time.