## 1 Introduction
Furthermore, [PERSON] et al. [16] calculated the total optical flow by computing the optical flow between three consecutive frames and combining the HS optical flow vector fields between the current frame and the previous frame and between the current frame and the next frame. Then, they used Otsu thresholding to detect moving objects with high detection accuracy; however, the secondary optical flow estimation significantly increased the time consumption. [PERSON] et al. [25] used the homography matrix to construct the optical flow background model and RANSAC to solve and optimize the model. Next, they determined the adaptive threshold of optical flow according to the estimated background model. The optical flow threshold size can be adaptively adjusted with the change in the camera motion. However, the algorithm accuracy is affected by the background model, and the moving object extraction is disturbed by the 'ghosting' problem. [PERSON] et al. [26] determined the average amplitude value of optical flow as the threshold and put results into the HSV color space to mark and remove the shadow. However, the detected moving object still has hole problems so the method is only used on a rigid moving object. Above all, the three issues in detecting moving objects by the optical flow algorithm should be resolved; meaning, first, the influence of illumination variation and the false detection of moving objects when there are illumination changes in the image sequence. Second, the 'ghosting' problem, due to the change in the position of the moving object which causes the background area to be covered by the region of the moving target in the previous frame while the background area appears in the next frame without being obscured and is mistakenly detected as the moving object area. Third, camera jittering or the movement that occurs when the camera may be non-fixed or dithered in acquiringimage frames, which can change the image sequence background. Still, the commonly used optical flow moving object detection method is only used for a fixed background. A moving object detection algorithm based on optical flow estimation and robust feature points was examined in this paper to address the problems of illumination variation, 'ghosting', dynamic texture interference, and camera jittering or movement. First, the algorithm predicts the image's optical flow field. Then, according to the optical flow residual principle, the local optical flow calculation of the Lucas-Kanade (LK) optical flow method is integrated into the Horn-Schunck (HS) optical flow method to constitute the optical flow equation data item to estimate the optical flow vector. Next, the Delaunay triangulation is applied to solve the motion occlusion problem, establishing the high-precision optical flow estimation energy equation. Furthermore, it is necessary to further threshold the optical flow vector field to determine the moving object's foreground area. The Otsu algorithm is often applied in the threshold processing of the optical flow method of moving object detection because it has better detection results in a fixed background environment. However, the object segmentation results are poor when the camera is jittering or moving. To solve this problem, this study proposes an optical flow threshold algorithm based on robust feature points. The threshold is determined according to the Harris robust feature points of the background area instead of the traditional optical flow Otsu threshold method to extract the foreground area of moving targets. The proposed algorithm can then be applied in either condition: when the camera is moving or when it is fixed. Importantly, this algorithm offers more accurate detection and extraction results for rigid and non-rigid targets.