Multiple Combined Constraints for Optical Flow Estimation

Several approaches to optical flow estimation use differential methods to model changes in image brightness over time. In computer vision it is often desirable to over constrain the problem to more precisely determine the solution and enforce robustness. In this paper, two new solutions for optical flow computation are proposed which are based on combining brightness and gradient constraints using more than one quadratic constraint embedded in a robust statistical function. Applying the same set of differential equations to different quadratic error functions produces different results. The two techniques combine the advantages of different constraints to achieve the best results. Experimental comparisons of estimation errors against those of wellknown synthetic ground-truthed test sequences showed good qualitative performance.