Tao Hu
ABSTRACT
In recent years, with the development of relevant technologies in the field of machine vision, the processing of visual image information has become the focus of research. Among them, the detection of moving targets is a very important research direction in the field of machine vision, which lays a foundation for the recognition of moving targets and tracking of moving targets. The task of moving target detection is to identify the physical movement of the target in a specific area. In this paper, the relevant image processing techniques used in the process of moving target detection are briefly described, including image preprocessing, image segmentation, feature extraction and so on. Then it describes the algorithms commonly used for moving target detection in recent years, including background difference method, inter-frame method, optical flow method, and compares the advantages and limitations of these methods. In view of the shortcomings of these methods, it summarizes the previous solutions. Finally, the improvement of these algorithms in recent years is pointed out.
- It_job. An overview of preprocessing methods for image processing[EB/OL].https://blog.csdn.net/it_job/article/details.2017-12-18.Google Scholar
- Zhang Zhongliang. A Review of image target recognition methods based on machine vision[J]. Technology and Innovation, 2016(14):32.Google Scholar
- Wang Jin, LI Shaohua, Xie Shouyong.Research on object recognition method based on machine vision[J]. Journal of Southwest Normal University (Natural Science edition), 2015, 40 (6):130.Google Scholar
- Houtekamer P L, Mitchell H L. A sequential ensemble kalman filters for atmo spheric data assimilation[J].Monthly Weather Review, 2001, 129 (1):123.Google ScholarCross Ref
- Hou Hongying, Gao Tian, Li Tao.Overview of image segmentation methods [J]. Computer knowledge and technology, 2019, 15 (5):176.Google Scholar
- Wang Qiuping, Zhang Zhixiang, Zhu Xufang. A review of image segmentation methods[J].Information Recording Materials, 2019, 20 (7):12.Google Scholar
- Wang Zhirui, Yan Cailiang. A review of image feature extraction methods [J]. Journal of jishou university (natural science edition), 2011, 32 (05): 43--47.Google Scholar
- Ohanian P P, Dubes R C. Performance evaluation for four classes of textural features[J]. Pattern Recognition, 1992, 25 (8):819.Google ScholarCross Ref
- Clausi D A, Yue B. Comparing co--occurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(1):215.Google ScholarCross Ref
- Song Jiasheng. Research on moving target detection and tracking algorithm in video sequence image[D].Guangzhou: South China University of Technology, 2014.Google Scholar
- Xu Guoliang, Zhou Hang, Yuan Liangyou. Using mixed gaussian and topological structure of the human "ghost" suppression algorithm [J/OL]. Journal of intelligent system: 1--9 [2020-07-22]. http://kns.cnki.net/kcms/detail/23.1538.TP.20200714.1417.018.html.Google Scholar
- Ci Wenyan. Review of methods for detecting moving targets [J]. Information Technology, 2016(12): 93--96+100.Google Scholar
- Zhang Dongmei, Wu Jie, Li Pitin. Overview of motion target detection Algorithms based on machine vision [J]. Intelligent Computer and Applications, 202, 10 (03): 192--195+201.Google Scholar
- Duan Suolin, Gao Renzhou, Liu Fu, Liu Maomao, Wang Yifan, Pan Lizheng. Prospect detection of fusion improved frame difference and visual background extraction algorithm [J]. Minicomputer system, 2019, 40 (09): 1903--1908.Google Scholar
- Li yuan, Hou honglu. Adaptive three-frame difference algorithm based on improved mean modeling[J]. Electronic measurement technology, 2019, 42 (03): 21--24.Google Scholar
- Zhang yanyan, Lou li, Liang Shuo. Research on moving Target Detection Technology based on Improved Optical Flow Algorithm[J]. Intelligent Computer and Application, 2012, 8 (01): 55--58.Google Scholar
- Guan Hongyun, Su Zhentao, Wang Chen. Background difference algorithm based on feature fusion [J/OL]. Electronic science and technology, 2020 (12): 1--6 [2020-07-22]. http://kns.cnki.net/kcms/detail/61.1291.TN.20200106.1536.016.html.Google Scholar
- Chen Yuan, Hu Na, Yu Qiuyue. Moving object Detection using background difference method and frame difference method[J]. Modern Computer, 2019(34): 50--53.Google Scholar
Index Terms
- A Survey of Moving Target Detection Methods Based on Machine Vision
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