基于端云协同的目标检测跟踪自适应调度算法  

Adaptive Scheduling Algorithm for Object Detection and Tracking Based on Device-Cloud Collaboration

作　　者：谭光 李昌镐 詹昭焕 TAN Guang;LI Changhao;ZHAN Zhaohuan(School of Intelligent Systems Engineering,Sun Yat-sen University,Shenzhen 518106,Guangdong, China)

机构地区：[1]中山大学智能工程学院,广东深圳518106

出　　处：《华南理工大学学报（自然科学版）》2021年第7期86-93,共8页Journal of South China University of Technology(Natural Science Edition)

基　　金：国家自然科学基金资助项目(61772509);广东省自然科学基金资助项目(2019A1515011066);深圳市基础研究重点项目(JCYJ20200109142217397)。

摘　　要：智能移动设备逐渐普及,然而视频图像检测等移动端应用仍然受到移动设备有限的计算和储存能力的限制,而且传统的云计算已无法满足这些应用对网络延迟、抖动、安全性等的需求。为此,文中首先设计了一个端云协同系统的用户体验指标,作为目标检测精度和能耗的综合衡量标准;然后将用户体验指标作为优化目标,提出了一种视频图像检测跟踪自适应调度算法,该算法通过预测网络带宽、计算传输时延来对目标检测和目标跟踪任务进行调度。在KITTI视频图像数据集上的实验结果表明,该检测跟踪自适应调度算法能够获得较高的用户体验,极大地减少了能量损耗,并获得78.3%的检测精度。As smart mobile devices become increasingly popular,mobile applications such as object detection in videos are greatly limited by the computing and storage capacity of mobile devices.Traditional cloud computing can not meet the requirements of applications for network delay,network jitter,security and other issues.For this purpose,this paper designed the quality of experience(QoE)as a comprehensive measure of object detection accuracy and energy consumption based on end-cloud collaborative system.Regarding the quality of experience as the optimization goal,this paper then proposed an adaptive scheduling algorithm for video image detection and tracking.The algorithm schedules object detection and object tracking by predicting network bandwidth and calculating transmission delay.Experimental results on the KITTI video dataset show that the detection-tracking adaptive scheduling algorithm can achieve a higher QoE value,significantly reduce the energy loss,and achieve an detection accuracy of 78.3%.

关 键 词：端云协同 用户体验指标 目标检测 目标跟踪 自适应调度 

分 类 号：TP393.09[自动化与计算机技术—计算机应用技术]