## 3 Experiment and Results
### Experiment and Analysis
This is because the optical flow amplitude of the head of the moving object is quite different from the body part, and the optical flow is also generated in the background. The Otsu can only segment a part of the foreground area with noticeable optical flow differences. This study's optical flow estimation algorithm presented a higher accuracy and more accurate final extraction result. In the Bamboo2 sequence, the optical flow amplitude in the foreground area is significantly higher than in the background area due to the weak optical flow in the background area; hence, Otsu performs better in the Alley2 sequence. Because the optical flow amplitude of the moving target head region is significantly lower than other target regions, the Otsu fails to detect the head region completely in optical flow estimation results. In contrast, this study's algorithm has a better segmentation. In the Bamboo2 sequence, some pieces of Bamboo2 are an occlusion obstacle as the background area generates optical flow, which should not be detected as the foreground area with the moving object, and the optical flow amplitude of the background area of the whole frame image is large. Those elements seriously interfere with the segmentation and extraction of the foreground area of the moving object. The threshold segmentation algorithm in this paper does not misinterpret the occlusion obstacle area in B&A and the enhanced optical flow estimation method presented in this study. In summary, the results of the robust feature threshold segmentation proposed in this study are significantly better than the commonly used Otsu threshold processing algorithm and more suitable than the Otsu threshold processing method for the HS pyramid, B&A, and Brox, which are commonly used optical flow estimation algorithms that can segment the moving target area more accurately in static or moving environments. Moreover, the enhanced optical flow estimation algorithms with the proposed robust feature threshold segmentation method in this study achieved better results. Morphological image processing is required because many problems accompany the detected moving object foreground area. Figure 9 compares the morphological processing results of different optical flow algorithms with the proposed robust feature threshold segmentation method. The top-to-bottom regions of Figure 9 depict the current image, the real foreground area of the moving object, the morphological image operation of HS, B&A, Brox, and this study's optical flow algorithm with threshold processing. The binary image of the foreground regions of the moving object extracted after morphological processing has no holes and irregular edges. The problem of small areas in the background area has also been addressed. Figure 10 shows the performance test results of the moving object detection method obtained by the proposed algorithm and the HS pyramid, B&A, and Brox after thresholding and morphological operation processing in this paper. Figure 10a shows the accuracy rate. Figure 10b shows the recall rate. Figure 10c exhibits the all-around performance. The higher the value, the better the performance. Finally, the accuracy of the proposed algorithm is verified. Figure 8: Comparison of the extraction results in a single moving target scene. (**I**) Otsu results and (**II**) our thresholding results. The top-to-bottom regions depict the LK result, HS result, B&A result, Brox result, and this study’s result. Figure 10: Performance comparison of moving object detection algorithms. (**a**) is the accuracy rate comparison result. (**b**) is the recall rate comparison result. (**c**) is the F-measure rate comparison result. Figure 9: Comparison of the HS pyramid, B&A, Brox, and the optical flow method with this study’s thresholding method and morphological operation results. The top-to-bottom regions present the current image, true moving target areas, HS result, B&A result, Brox result, and this study’s results.