基于云边协同的煤矿井下尺度自适应目标跟踪方法

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

  • 摘要: 煤矿井下监控视频中的运动目标通常存在较大的尺度变化和形变,导致基于计算机视觉的目标跟踪算法准确率不高,且海量的视频数据导致基于云端的集中式数据处理方式难以满足目标跟踪的实时性要求。针对上述问题,提出了一种基于云边协同的煤矿井下尺度自适应目标跟踪方法。设计了基于深度估计的尺度自适应目标跟踪算法,通过构建深度−尺度估计模型,利用目标深度值估计尺度值,实现尺度自适应目标跟踪,解决了目标尺度变化和形变导致跟踪准确率不高的问题;设计了一种基于云边协同的智能监控系统架构,将尺度自适应目标跟踪算法细粒度划分后的子模块按所需计算资源分别部署在系统的边缘端和云端,通过边缘端和云端的分布式并行处理提高算法运行效率,解决了集中式数据处理方式实时性差的问题。将基于云边协同的煤矿井下尺度自适应目标跟踪方法应用于煤矿井下视频序列,对其跟踪性能和实时性能进行实验验证,结果表明:与核相关滤波(KCF)、判别型尺度空间跟踪(DSST)算法、基于多特征融合的尺度自适应(SAMF)算法3种经典目标跟踪算法相比,基于深度估计的尺度自适应目标跟踪算法在煤矿井下目标出现较大尺度变化和形变时,具有更高的跟踪精度和成功率;与传统的云计算处理方式相比,基于云边协同的尺度自适应目标跟踪算法部署方式使算法总时延降低了32.55%,有效提升了煤矿井下智能监控系统目标跟踪的实时性能。

     

    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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