A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks
Aiming at the problem of real-time detection and location of moving objects, the deep learning algorithm is used to detect moving objects in complex situations. In this paper, based on the deep learning algorithm of wireless sensor networks, a novel target motion detection method is proposed. This m...
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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/10.1155/2021/5518196 |
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| _version_ | 1832560089757646848 |
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| author | Linghua Zhao Zhihua Huang |
| author_facet | Linghua Zhao Zhihua Huang |
| author_sort | Linghua Zhao |
| collection | DOAJ |
| description | Aiming at the problem of real-time detection and location of moving objects, the deep learning algorithm is used to detect moving objects in complex situations. In this paper, based on the deep learning algorithm of wireless sensor networks, a novel target motion detection method is proposed. This method uses the deep learning model to extract visual potential representation features through offline similarity function ranking learning and online model incremental update and uses the hierarchical clustering algorithm to achieve target detection and positioning; the low-precision histogram and high-precision histogram cascade the method which determines the correct position of the target and achieves the purpose of detecting the moving target. In order to verify the advantages and disadvantages of the deep learning algorithm compared with traditional moving object detection methods, a large number of comparative experiments are carried out, and the experimental results were analyzed qualitatively and quantitatively from a statistical perspective. The results show that, compared with the traditional methods, the deep learning algorithm based on the wireless sensor network proposed in this paper is more efficient. The detection and positioning method do not produce the error accumulation phenomenon and has significant advantages and robustness. The moving target can be accurately detected with a small computational cost. |
| format | Article |
| id | doaj-art-14d6ab6d4aff4e81a4173aaad8812d22 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-14d6ab6d4aff4e81a4173aaad8812d222025-02-03T01:28:23ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55181965518196A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor NetworksLinghua Zhao0Zhihua Huang1Xiangsihu College of Guangxi University for Nationalities, Nanning, Guangxi 530008, ChinaDepartment of Sport, Guangxi University, Nanning, Guangxi 530004, ChinaAiming at the problem of real-time detection and location of moving objects, the deep learning algorithm is used to detect moving objects in complex situations. In this paper, based on the deep learning algorithm of wireless sensor networks, a novel target motion detection method is proposed. This method uses the deep learning model to extract visual potential representation features through offline similarity function ranking learning and online model incremental update and uses the hierarchical clustering algorithm to achieve target detection and positioning; the low-precision histogram and high-precision histogram cascade the method which determines the correct position of the target and achieves the purpose of detecting the moving target. In order to verify the advantages and disadvantages of the deep learning algorithm compared with traditional moving object detection methods, a large number of comparative experiments are carried out, and the experimental results were analyzed qualitatively and quantitatively from a statistical perspective. The results show that, compared with the traditional methods, the deep learning algorithm based on the wireless sensor network proposed in this paper is more efficient. The detection and positioning method do not produce the error accumulation phenomenon and has significant advantages and robustness. The moving target can be accurately detected with a small computational cost.http://dx.doi.org/10.1155/2021/5518196 |
| spellingShingle | Linghua Zhao Zhihua Huang A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks Complexity |
| title | A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks |
| title_full | A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks |
| title_fullStr | A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks |
| title_full_unstemmed | A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks |
| title_short | A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks |
| title_sort | moving object detection method using deep learning based wireless sensor networks |
| url | http://dx.doi.org/10.1155/2021/5518196 |
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