A Novel Moving Object Detection Algorithm Based on Robust Image Feature Threshold Segmentation with Improved Optical Flow Estimation

Abstract

The detection of moving objects in images is a crucial research objective; however, several challenges, such as low accuracy, background fixing or moving, ‘ghost’ issues, and warping, exist in its execution. The majority of approaches operate with a fixed camera. This study proposes a robust feature threshold moving object identification and segmentation method with enhanced optical flow estimation to overcome these challenges. Unlike most optical flow Otsu segmentation for fixed cameras, a background feature threshold segmentation technique based on a combination of the Horn–Schunck (HS) and Lucas–Kanade (LK) optical flow methods is presented in this paper. This approach aims to obtain the segmentation of moving objects. First, the HS and LK optical flows with the image pyramid are integrated to establish the high-precision and anti-interference optical flow estimation equation. Next, the Delaunay triangulation is used to solve the motion occlusion problem. Finally, the proposed robust feature threshold segmentation method is applied to the optical flow field to attract the moving object, which is the. extracted from the Harris feature and the image background affine transformation model. The technique uses morphological image processing to create the final moving target foreground area. Experimental results verified that this method successfully detected and segmented objects with high accuracy when the camera was either fixed or moving.

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“…In order to quantitatively analyze the comparison between our method and other methods, we used precision, mean pixel intersection over union (mIoU), and the F1 score (F1) as evaluation indicators. The formulae for calculating these metrics are shown in Equations ( 19)- (21).…”

“…Precision is defined as Precision = TP TP + FP (21) where TP, FP, FN, and k represent true positive, false positive, true negative, false negative, and the number of categories, respectively.…”

“…Optical flow estimation is commonly employed in the analysis of moving objects [21]. Figure 6 and Table 2 in reference [22] use fusion segmentation and redistribution strategies to segment images and effectively generate motion components to calculate scene flow.…”

“…Optical flow estimation plays an important role in image segmentation due to its sensitivity to changes in light and shade in image textures and object shapes [21]. Optical flow estimation tracks pixel movements and changes within an image, thereby proving invaluable for tackling a multitude of segmentation challenges, including foregroundbackground separation, object tracking, and boundary detection [22].…”