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

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...

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Vydáno v:Applied sciences Ročník 13; číslo 8; s. 4854
Hlavní autoři: Ding, Jing, Zhang, Zhen, Yu, Xuexiang, Zhao, Xingwang, Yan, Zhigang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.04.2023
Témata:
Accuracy
Algorithms
Analysis
Cameras
computer vision
Image processing
image threshold
Medical imaging equipment
moving object detection
optical flow
Pedestrians
Sensors
Surveillance
ISSN:2076-3417, 2076-3417
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Shrnutí: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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ISSN:2076-3417
2076-3417
DOI:10.3390/app13084854