Local texture based optical flow for complex brightness variations

In real-world scenarios, complex brightness variations are commonly seen, due to shadows, global illumination changes and nonlinear camera responses, etc. Classical optical flow methods based on brightness or gradient constancy assumption tends to fail under these circumstances. This work proposes an image texture descriptor called LSOT, based on the local spatial structure of a pixel and the relative ordinal information. Then a texture constancy assumption is embedded into a variational optical flow estimation framework as a data term, in order to cope with complex brightness variations. In addition, a non-local regularization term is used to improve the accuracy of the obtained flow fields. The energy functional is optimized using a primal-dual algorithm in a coarse-to-fine warping fashion. Experimental results on synthetic and real image sequences demonstrate the superior performance of the proposed method.