High accuracy optical flow estimation based on PDE decomposition
Many optical flow estimation techniques are based on the differential optical flow equation. These algorithms involve solving over-determined systems of optical flow equations. Least-squares (LS) estimation is usually used to solve these systems even though the underlying noise does not conform to the model implied by LS estimation. To tackle this problem, work has been done combining the variational partial differential equation (PDE) methods with motion estimation. However, PDE methods demonstrated powerful tools to decompose an image into its structures, textures, and noise components. The noise is eliminated systematically in the proposed scheme, and the optical flow is computed separately on both components of the decomposition. We experimentally show that very precise and robust estimation of optical flow can be achieved with a total variational approach in real time. The implementation is described in a detailed way, which enables reimplementation of this high-end method. The proposed technique has been tested upon different dataset of both synthetic and real image sequences, and compared to both well-known and state-of-the-art differential optical flow methods.
optical flow pde motion estimation variational methods computer vision