Motion estimation approach based on dual-tree complex wavelets

The loss of information due to occlusion and other complications has been one of the main bottlenecks in the field of motion estimation. In this paper, we propose a novel motion estimation algorithm based on dual-tree complex wavelet (DT-CWT), which utilizes its approximate shift invariance and directional selectivity. Subbands within different orientations and levels are individually treated to overcome the issue of error propagation induced by frequently used coarse-to-fine searching strategy, as these subbands are approximately independent from each other. Then, the frame next to the current one is introduced to supplement the lost information due to the occlusion and to improve the estimation accuracy within boundary areas. Experimental results show that the proposed method is effective with robustness to intense motion, scene change and occlusion.