Research on HS Optical Flow Algorithm Based on Motion Estimation Optimization

Abstract

The amount of computation for detecting moving objects by the optical flow algorithm is large. The optical flow information in the smooth region cannot be detected by the optical flow algorithm, and it is susceptible to noise in a complicated environment. In this study, an optimized Horn-Schunck (HS) optical flow algorithm based on motion estimation is proposed. To detect Harris corner in the image, the proposed algorithm is used in combination with the motion estimation algorithm based on macroblock to determine the region of interest (ROI) [1]. The ROI is then used as the initial motion vector for HS calculation to obtain the optical flow information. Filtering is conducted to eliminate the background noise. Experimental result shows that the application of the proposed algorithm improves the computational speed, avoids the interference of background noise, and enhances the robustness of HS. Moreover, the algorithm solves the problem rooted in the inability of the HS algorithm to detect the smooth part of optical flow information [2].

Adaptive Fractional-Order Multi-Scale Optimization TV-L1 Optical Flow Algorithm

“…where ∇u = u − u, ∇v = v − v, u, and v respectively represent the mean velocity in the x and y directions in the neighborhood; then, the components of the optical flow vector in the x and y directions can be obtained as Equations ( 5) and (6).…”

“…This cumulative error significantly impacts subsequent recognition results. Reference [6] employed multi-channel sampling interpolation to restore texture in motion-blurred images while reference [7] proposed a pixel-weight-based smoothing operation aimed at reducing edge blur caused by variational light path equations. Based on the work of Demetz O et al, reference [8] proposed the anisotropic smoothness constraint.…”