Motion Estimation for Dynamic Texture Videos Based on Locally and Globally Varying Models

Motion estimation, i.e., optical flow, of fluid-like and dynamic texture (DT) images/videos is an important challenge, particularly for understanding outdoor scene changes created by objects and/or natural phenomena. Most optical flow models use smoothness-based constraints using terms such as fluidity from the fluid dynamics framework, with constraints typically being incompressibility and low Reynolds numbers (Re ). Such constraints are assumed to impede the clear capture of locally abrupt image intensity and motion changes, i.e., discontinuities and/or high Re over time. This paper exploits novel physics-based optical flow models/constraints for both smooth and discontinuous changes using a wave generation theory that imposes no constraint on Re or compressibility of an image sequence. Iterated two-step optimization between local and global optimization is also used: first, an objective function with varying multiple sine/cosine bases with new local image properties, i.e., orientation and frequency, and with a novel transformed dispersion relationship equation are used. Second, the statistical property of image features is used to globally optimize model parameters. Experiments on synthetic and real DT image sequences with smooth and discontinuous motions demonstrate that the proposed locally and globally varying models outperform the previous optical flow models.