Volume 49 Issue 4
Apr.  2023
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Article Navigation >  Journal of Mine Automation  > 2023  >  49(4): 50-61
MU Qi, HAN Jiajia, ZHANG Han, et al. A scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration[J]. Journal of Mine Automation，2023，49(4)：50-61.  doi: 10.13272/j.issn.1671-251x.2022100093
Citation: MU Qi, HAN Jiajia, ZHANG Han, et al. A scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration[J]. Journal of Mine Automation，2023，49(4)：50-61.  doi: 10.13272/j.issn.1671-251x.2022100093
shu

A scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration

doi: 10.13272/j.issn.1671-251x.2022100093
  • 1.

    College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China

  • 2.

    College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

  • Received Date: 2022-10-31
  • Rev Recd Date: 2023-04-08
    • Available Online: 2023-04-27
    Abstract
  • 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.

     

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