A Novel Moving Object Detection Algorithm Based on Robust Image Feature Threshold Segmentation with Improved Optical Flow Estimation
-
Published:2023-04-12
Issue:8
Volume:13
Page:4854
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Ding Jing12ORCID, Zhang Zhen2ORCID, Yu Xuexiang12, Zhao Xingwang2, Yan Zhigang3
Affiliation:
1. Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan 232001, China 2. School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China 3. School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
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.
Funder
Coal Industry Engineering Research Center of Mining Area Environmental And Disaster Co-operative Monitoring (Anhui University of Science and Technology Key Research and Development Program of Anhui Province
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference42 articles.
1. Kulchandani, J.S., and Dangarwala, K.J. (2015, January 8–10). Moving object detection: Review of recent research trends. Proceedings of the 2015 International Conference on Pervasive Computing (ICPC), Pune, India. 2. Zhan, C., Duan, X., Xu, S., Song, Z., and Luo, M. (2007, January 22–24). An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection. Proceedings of the Fourth International Conference on Image and Graphics (ICIG 2007), Chengdu, China. 3. Moving objects detection with a moving camera: A comprehensive review;Chapel;Comput. Sci. Rev.,2020 4. New trends on moving object detection in video images captured by a moving camera: A survey;Yazdi;Comput. Sci. Rev.,2018 5. Stojnić, V., Risojević, V., Muštra, M., Jovanović, V., Filipi, J., Kezić, N., and Babić, Z. (2021). A Method for Detection of Small Moving Objects in UAV Videos. Remote Sens., 13.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|