面向边缘计算的目标追踪应用部署策略研究

目标追踪算法虽已在诸多领域得到广泛应用,然而由于实时性和功耗问题,使得基于深度学习模型的算法难以在移动终端设备上部署应用.结合边缘计算技术,从应用部署优化的角度,对目标追踪算法在移动设备上的部署策略进行研究.通过对目标追踪应用特点、移动设备特性以及边缘云网络架构的分析,提出一种面向边缘计算的目标追踪应用部署策略.通过任务分割策略,将目标追踪应用的计算任务合理卸载至边缘云,并利用信息融合策略对计算结果进行分析融合;此外,利用运动检测,进一步降低终端节点的计算压力和功耗.通过对不同部署策略进行对比实验,结果表明:相比计算任务本地计算,该部署策略明显降低了任务响应时间;相比完全卸载至边缘云,该部署策略降低了相同计算任务的处理时间.

Research on Target Tracking Application Deployment Strategy for Edge Computing

Target tracking algorithm has been widely used in many fields. However, due to the problems of real-time and power consumption, it is difficult to deploy the algorithm based on deep learning model on mobile terminal devices. This paper studies the deployment strategy of target tracking algorithm on mobile devices from the perspective of application deployment optimization combined with edge computing technology. A deployment strategy of target tracking application oriented to edge computing is proposed based on the analysis of device characteristics and edge cloud network architecture,. The computing task of target tracking application is reasonably unloaded to edge cloud by task segmentation strategy and the computing results are analyzed and fused by the information fusion strategy. In addition, a motion detection scheme is proposed to further reduce the computing pressure and power consumption of terminal nodes The experimental results show that compared with local computing, the deployment strategy significantly reduces the response time of the task, and compared with completely uninstalling to the edge cloud, the deployment strategy reduces the processing time of the same computing task.