Multi-scale optical flow estimation of the video based on gradient optimization

In this paper, a new algorithm based on gradient optimization is proposed for the purpose of optical flow estimation of video images with different motion ranges. The algorithm first filters the original video images with Log-gabor filter from different phases and scales. Then the spatio-temporal gradient is calculated by using the feature images obtained. Finally optical flow is calculated in the light of the spatiotemporal gradient. In the meanwhile, the video images are layered and processed by the algorithm using coarse-to-fine image pyramid method. Both theory and experiment show that the algorithm is applied for the video optical flow motion estimation of the significant range. The video images gained are not only suitable for characteristics of the human visual resolution, but also the spatio-temporal gradient is more optimized, and optical flow calculation is more accurate. Moreover, the time complexity of this algorithm is equivalent to that of the traditional optical flow method, and accuracy of the algorithm is superior to the methods suggested by Horn-Schunck, Duan et al.