A scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration
The moving targets in coal mine underground monitoring videos often have significant scale changes and deformations. This results in low accuracy of target tracking algorithms based on computer vision. Moreover, the massive amount of video data makes it difficult for centralized cloud-based data pro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2023-04-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2022100093 |
| _version_ | 1797821454356578304 |
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| author | MU Qi HAN Jiajia ZHANG Han LI Zhanli |
| author_facet | MU Qi HAN Jiajia ZHANG Han LI Zhanli |
| author_sort | MU Qi |
| collection | DOAJ |
| description | The moving targets in coal mine underground monitoring videos often have significant scale changes and deformations. This results in low accuracy of target tracking algorithms based on computer vision. Moreover, the massive amount of video data makes it difficult for centralized cloud-based data processing methods to meet the real-time requirements of target tracking. In order to solve the above problems, a scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration is proposed. A scale-adaptive target tracking algorithm based on depth estimation is designed. The scale-adaptive target tracking is achieved by constructing a depth-scale estimation model, which uses target depth values to estimate scale values. The problem of low tracking accuracy caused by target scale change and deformation is solved. An intelligent monitoring system architecture based on cloud-edge collaboration is designed. The sub-modules of the scale-adaptive target tracking algorithm, which are divided into fine granularity, are deployed at the edge and cloud of the system according to the required computing resources. The algorithm's operational efficiency is improved through distributed parallel processing at the edge and cloud, solving the problem of poor real-time performance in the centralized data processing. The scale-adaptive target tracking method based on cloud-edge collaboration is applied in coal mine underground video sequences. The tracking performance and real-time performance are verified experimentally. The results show that compared with three classic target tracking algorithms, namely kernel correlation filter (KCF), discriminant scale space tracking (DSST) algorithm, and scale adaptive multiple feature (SAMF) algorithm, the scale-adaptive target tracking algorithm based on depth estimation has higher tracking precision and success rate when there are significant scale changes and deformations in coal mine underground targets. Compared with traditional cloud computing processing methods, the deployment method of scale-adaptive target tracking algorithm based on cloud-edge collaboration reduces the total delay of the algorithm by 32.55%. It effectively improves the real-time performance of target tracking of intelligent monitoring system in coal mine underground. |
| first_indexed | 2024-03-13T09:52:56Z |
| format | Article |
| id | doaj.art-d23c61d461964f34b670cbf04185b6eb |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-03-13T09:52:56Z |
| publishDate | 2023-04-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-d23c61d461964f34b670cbf04185b6eb2023-05-24T06:23:30ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2023-04-01494506110.13272/j.issn.1671-251x.2022100093A scale-adaptive target tracking method for coal mine underground based on cloud-edge collaborationMU QiHAN Jiajia0ZHANG Han1LI Zhanli2College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, ChinaCollege of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, ChinaCollege of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, ChinaThe moving targets in coal mine underground monitoring videos often have significant scale changes and deformations. This results in low accuracy of target tracking algorithms based on computer vision. Moreover, the massive amount of video data makes it difficult for centralized cloud-based data processing methods to meet the real-time requirements of target tracking. In order to solve the above problems, a scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration is proposed. A scale-adaptive target tracking algorithm based on depth estimation is designed. The scale-adaptive target tracking is achieved by constructing a depth-scale estimation model, which uses target depth values to estimate scale values. The problem of low tracking accuracy caused by target scale change and deformation is solved. An intelligent monitoring system architecture based on cloud-edge collaboration is designed. The sub-modules of the scale-adaptive target tracking algorithm, which are divided into fine granularity, are deployed at the edge and cloud of the system according to the required computing resources. The algorithm's operational efficiency is improved through distributed parallel processing at the edge and cloud, solving the problem of poor real-time performance in the centralized data processing. The scale-adaptive target tracking method based on cloud-edge collaboration is applied in coal mine underground video sequences. The tracking performance and real-time performance are verified experimentally. The results show that compared with three classic target tracking algorithms, namely kernel correlation filter (KCF), discriminant scale space tracking (DSST) algorithm, and scale adaptive multiple feature (SAMF) algorithm, the scale-adaptive target tracking algorithm based on depth estimation has higher tracking precision and success rate when there are significant scale changes and deformations in coal mine underground targets. Compared with traditional cloud computing processing methods, the deployment method of scale-adaptive target tracking algorithm based on cloud-edge collaboration reduces the total delay of the algorithm by 32.55%. It effectively improves the real-time performance of target tracking of intelligent monitoring system in coal mine underground.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2022100093intelligent monitoring of minesvideo monitoringtarget trackingdepth scale estimationscale-adaptationcloud-edge collaborationtask unloading |
| spellingShingle | MU Qi HAN Jiajia ZHANG Han LI Zhanli A scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration Gong-kuang zidonghua intelligent monitoring of mines video monitoring target tracking depth scale estimation scale-adaptation cloud-edge collaboration task unloading |
| title | A scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration |
| title_full | A scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration |
| title_fullStr | A scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration |
| title_full_unstemmed | A scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration |
| title_short | A scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration |
| title_sort | scale adaptive target tracking method for coal mine underground based on cloud edge collaboration |
| topic | intelligent monitoring of mines video monitoring target tracking depth scale estimation scale-adaptation cloud-edge collaboration task unloading |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2022100093 |
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