## 4 Conclusions  
This paper proposes a robust feature threshold segmentation based on improved optical flow estimation to detect moving objects. Several experiments were conducted to prove the effectiveness of this study's algorithm. This method exhibits obvious superiority compared with the representative methods based on optical flow as follows:  
(1) The improved optical flow estimation method combined with HS and LK optical flow methods to reduce the effects of background noise and dynamic texture and improve the robustness and accuracy of optical flow estimation. Additionally, the Delaunay triangulation is introduced to reduce the inaccurate estimation of optical flow caused by the moving object 'ghost' problem. The algorithm can obtain high precision robust dense optical flow vector fields which can improve the accuracy and robustness of moving object detection.  
(2) A robust feature threshold method was proposed, instead of the commonly used Otsu method, by extracting the feature points and calculating the affine transformation matrix of the background model. Then, the robust feature points were obtained which fulfilled the background model using the robust point optical flow value as a threshold to attract the moving object. Our robust feature threshold method can be used in both static and moving environments. Experimental results show that the threshold greatly improves the range of application scenes of moving object detection based on optical flow estimation.  
Although this study has improved the moving object detection algorithm accuracy and application compared with the common moving object detection methods based on optical flow, there are still many aspects to be further studied. In the future, we will focus on the following research:  
(1) This study did not identify different moving objects. Therefore, based on the research in this paper, using the motion optical flow information further, to conduct optical flow classification and moving behavior analysis, to achieve multi-objective detailed recognition will be the objective of future works.  
(2) Robust feature threshold segmentation combined with an intensive optical flow estimation method based on deep learning will be considered as well as combining the Delaunay triangulation with the convolutional neural network to improve the efficiency of moving object detection in future work.  
Conceptualization, J.D., Z.Z. and Z.Y.; methodology, J.D.; validation, J.D., Z.Z. and X.Y.; data curation, J.D. and X.Z.; writing--original draft preparation, J.D.; writing--review and editing, J.D.; visualization, J.D.; supervision, Z.Y.; project administration, X.Y.; funding acquisition, J.D., X.Y. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring (Anhui University of Science and Technology) (Grant NO. KSATJC202205), Major science and technology projects of Anhui Province (Grant NO. 202103a05020026), Key Research and Development Program of Anhui Province (Grant NO. 202104a07020014), Major Project on Natural Science Foundation of Universities in Anhui Province (Grant NO. 2022 AH040111), Anhui University of Science and Technology Talent Introduction Research Start-up (Grant NO. 2022 jjrc96), and Natural Science Foundation Project of Anhui Province (Grant NO. 2008085 MD114).  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[http://sintel.is.tue.mpg.de/downloads](http://sintel.is.tue.mpg.de/downloads)]([http://sintel.is.tue.mpg.de/downloads](http://sintel.is.tue.mpg.de/downloads)) (accessed on 8 April 2023).  
**Acknowledgments:** We want to express our sincere gratitude to the anonymous reviewers and editors for their efforts in the improvement of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.