Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model
Aiming at the shortcomings of traditional moving target detection methods in complex scenes such as low detection accuracy and high complexity, and not considering the overall structure information of the video frame image, this paper proposes a moving-target detection based on sensor network. First...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/3909522 |
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| _version_ | 1832566489472827392 |
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| author | Qichang Xu |
| author_facet | Qichang Xu |
| author_sort | Qichang Xu |
| collection | DOAJ |
| description | Aiming at the shortcomings of traditional moving target detection methods in complex scenes such as low detection accuracy and high complexity, and not considering the overall structure information of the video frame image, this paper proposes a moving-target detection based on sensor network. First, a low-power motion detection wireless sensor network node is designed to obtain motion detection information in real time. Secondly, the background of the video scene is quickly extracted by the time domain averaging method, and the video sequence and the background image are channel-merged to construct a deep full convolutional network model. Finally, the network model is used to learn the deep features of the video scene and output the pixel-level classification results to achieve moving target detection. This method not only can adapt to complex video scenes of different sizes but also has a simple background extraction method, which effectively improves the detection speed. |
| format | Article |
| id | doaj-art-85d8bd749e764c72bfaf3f0c8cd4786d |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-85d8bd749e764c72bfaf3f0c8cd4786d2025-02-03T01:03:58ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/39095223909522Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network ModelQichang Xu0Institute of Physical Education, Hechi University, Yizhou, Guangxi 546300, ChinaAiming at the shortcomings of traditional moving target detection methods in complex scenes such as low detection accuracy and high complexity, and not considering the overall structure information of the video frame image, this paper proposes a moving-target detection based on sensor network. First, a low-power motion detection wireless sensor network node is designed to obtain motion detection information in real time. Secondly, the background of the video scene is quickly extracted by the time domain averaging method, and the video sequence and the background image are channel-merged to construct a deep full convolutional network model. Finally, the network model is used to learn the deep features of the video scene and output the pixel-level classification results to achieve moving target detection. This method not only can adapt to complex video scenes of different sizes but also has a simple background extraction method, which effectively improves the detection speed.http://dx.doi.org/10.1155/2021/3909522 |
| spellingShingle | Qichang Xu Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model Complexity |
| title | Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model |
| title_full | Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model |
| title_fullStr | Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model |
| title_full_unstemmed | Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model |
| title_short | Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model |
| title_sort | using sensor network in motion detection based on deep full convolutional network model |
| url | http://dx.doi.org/10.1155/2021/3909522 |
| work_keys_str_mv | AT qichangxu usingsensornetworkinmotiondetectionbasedondeepfullconvolutionalnetworkmodel |