## 3 Experiment and Results
### Experiment and Analysis  
Figure 6 shows the comparison of optical flow estimation. The left to right sides depict the sequence images of Alley2, Ambush4, and Bamboo2 in the MPI Sintel Flow Final dataset, and the moving objects are all non-rigid. The up-to-down areas of Figure 6 represent the current frame image, the next frame image, the true optical flow of the current frame, and the optical flow field calculated by LK, HS pyramid, B&A, Brox optical flow estimation, and the improved optical flow calculation. The optical flow estimation results reveal that the sparse optical flow value obtained by the LK method cannot determine the complete optical flow of the moving object area; hence, it is tricky to extract the moving target area of the optical flow threshold. The optical flow in the background (Bamboo2) or the moving target area cannot be distinguished in the Bamboo2 sequence images. The HS pyramid method produces more blurred results at the images' edges, and there is a false optical flow detection in the background area in the+Ambush4 sequence images. The B&A method has a better optical flow estimation result in the edge area than the HS pyramid method. However, the optical flow estimation error is too large when the optical flow value of the non-rigid moving target area is quite different, such as in the Ambush4 sequence. The Brox algorithm has apparent errors. The proposed improved optical flow estimation algorithm exhibits a higher accuracy and better edge retention. The optical flow effect is also better when the optical flow values have large differences in the moving object area. In addition, the HS and LK optical flows were combined to deal with the problem of an optical flow calculation error in dynamic texture regions because of the optical flow estimation. There was no problem with optical flow error detection in the background area of Ambush4 when the environment was complex and had fog and snow. The optical flow detection effect of the moving target showing in Bamboo2 is also better when the domain occludes it. Figure 7 illustrates the EPE and AAE comparisons of the optical flow estimated results in Figure 6. It also shows that the two indexes of the optical flow estimation algorithm in this paper are lower than other algorithms for different image sequences with a higher estimated optical flow accuracy. Figure 6: Comparison results of optical flow estimation in a single moving target scene. From left to right are the Alley2, Ambush4, and Bamboos2 datasets. The top-to-bottom areas show the current image, the next image, ground flow results, LK results, HS results, B&A results, Brox results, and this study’s results. Figure 7: Error comparison of optical flow estimation algorithms. (**a**) is the EPE comparison result and (**b**) is the AAE comparison result. Figure 8 displays the segmentation results of moving target extraction corresponding to the different optical flow field estimations of each image frame in Figure 6. The robust feature threshold segmentation method was compared with the Otsu threshold method to prove its effectiveness and applicability. This method was also performed on different optical flow estimation methods such as LK, B&A, HS pyramidal, Brox, and this study's results. The findings proved that this study's robust feature threshold segmentation method could be applied to different optical flow estimation algorithms. Figure 8 presents the Otsu threshold moving object extraction results and Figure 8 represents the results of this study's robust Harris feature threshold moving object extraction. According to the LK optical flow thresholding segmentation results of three image sequences, neither Otsu nor the proposed algorithm is suitable for sparse optical flow thresholds to extract moving objects. In the Alley2 sequence, the Otsu and this study's threshold method failed because of the error of the optical flow estimation by the Brox algorithm. Otsu can only extract a part of the moving object, but this study's method achieved better extraction results.