计算机科学 ›› 2021, Vol. 48 ›› Issue (4): 180-186.doi: 10.11896/jsjkx.201200116

• 计算机图形学&多媒体 • 上一篇    下一篇

多分辨率下资源感知的图像目标自适应缩放检测

张开强1, 蒋从锋1, 程小兰1, 贾刚勇1, 张纪林2, 万健3   

  1. 1 杭州电子科技大学计算机学院 杭州310018
    2 杭州电子科技大学网络空间安全学院 杭州310018
    3 浙江科技学院信息与电子工程学院 杭州310023
  • 收稿日期:2020-06-24 修回日期:2021-03-01 出版日期:2021-04-15 发布日期:2021-04-09
  • 通讯作者: 蒋从锋(cjiang@hdu.edu.cn)
  • 基金资助:
    国家自然科学基金面上项目(61972118,61972358),浙江省重点研发计划项目(2019C01059)

Resource-aware Based Adaptive-scaling Image Target Detection Under Multi-resolution Scenario

ZHANG Kai-qiang1, JIANG Cong-feng1, CHENG Xiao-lan1, JIA Gang-yong1, ZHANG Ji-lin2, WAN Jian3   

  1. 1 School of Computer Science and Technology,Hangzhou Dianzi University,Hangzhou 310018,China
    2 School of Cyber Science and Engineering,Hangzhou Dianzi University,Hangzhou 310018,China
    3 School of Information and Electronic Engineering,Zhejiang University of Science and Technology,Hangzhou 310023,China
  • Received:2020-06-24 Revised:2021-03-01 Online:2021-04-15 Published:2021-04-09
  • About author:ZHANG Kai-qiang,born in 1997,postgraduate,is a student member of China Computer Federation.His main research interests include edge computing and system optimization.(zkqcs@hdu.edu.cn)
    JIANG Cong-feng,born in 1980,Ph.D,professor,is a member of China Computer Federation.His main research interests include edge computing,system optimization,performance evaluation and distributed system benchmarking.
  • Supported by:
    General Program of National Natural Science Foundation of China(61972118,61972358) and Zhejiang Key Research and Development Project of China(2019C01059).

摘要: 边缘视频处理可以降低云平台视频处理系统的视频传输时延、视频处理开销和存储成本,但是视频参数(分辨率、帧率等)的多样性容易导致边缘视频处理的效果不尽人意。通常,在图像预处理阶段会先对图像进行缩放变换再进行后续处理,以保障图像处理的最佳效果,但是在视频监控等具有不确定性的场景中对所有分辨率的图像直接成倍缩小容易降低目标检测率。基于以上问题,把图像水平像素点和垂直像素点的缩放倍数记作图像缩放因子,对于不同分辨率的视频数据,分析了图像缩放因子对视频数据处理效果的影响,提出了图像缩放因子动态设置方案。该方案以系统性能指标(服务器端系统功耗和内存使用率)为视频处理性能指标(人脸检测率)的约束条件,获取该分辨率下人脸检测率最优时对应的图像缩放因子。实验结果表明,对于不同分辨率的视频数据,图像缩放因子动态设置方案可以在保证视频处理性能的基础上,减少系统功耗和内存使用率,提高视频处理效率。

关键词: 边缘计算, 人脸检测, 视频参数, 视频处理, 图像缩放

Abstract: Edge video processing can reduce the video transmission delay,video processing overhead and storage cost in the video processing system of cloud platform,but the diversity of video parameters (resolution,frame rate,etc.) will lead to unsatisfactory effect of edge video processing.Usually,in the stage of image pre-processing,image scaling and transformation will be applied to ensure the best effect of image processing.However,in uncertain scenes such as video monitoring,the target detection rate can be reduced largely by directly scaling down the resolution of all the images.According to the problems mentioned above,this paper chooses the scaling ratio of the horizontal and vertical pixels as image scaling factor.For the video data with different resolutions,it analyzes the influence of the image scaling factor on the performance of video data processing and puts forward the dynamically-setting scheme of image scaling factor.In this scheme,the system performance index (system power consumption and memory utilization of the server-side) is taken as the constraint condition of video processing performance index (face detection rate),to get the image scaling factor corresponding to the optimal face detection rate with this resolution.Experimental results show that for video data with different resolutions,the dynamic setting scheme of image scaling factor can reduce system power consumption and memory utilization,improve video processing efficiency and capability while guaranteeing video processing performance.

Key words: Edge computing, Face detection, Image scaling, Video parameters, Video processing

中图分类号: 

  • TP391
[1]ZHOU B,DASTJERDI A V,CALHEIROS R N,et al.mCloud:A context-aware offloading framework forheterogeneous mobile cloud [J].IEEE Transactions on Services Computing,2017,10(5):797-810.
[2]LIU W,HUANG Y C,DU W,et al.Resource-Constrained Serial Task Offload Strategy in Mobile Edge Computing [J].Journal of Software,2020,31(6):1889-1908.
[3]MA H R,CHEN X,ZHOU Z,et al.Dynamic Task Offloading for Mobile Edge Computing with Green Energy [J].Journal of Computer Research and Development,2020,31(6):1889-1908.
[4]LIU Y,XU C,ZHAN Y,et al.Incentive mechanism for computation offloading using edge computing:a Stackelberg game approach [J].Computer Networks,2017,129:399-409.
[5]ME E,TODD T D,ZHAO D,et al.Energy awareoffloading for competing users on a shared communication channel [J].IEEE Transactions on Mobile Computing,2017,16(1):87-96.
[6]CHEN X.Decentralized computation offloading game for mobile cloud computing [J].IEEE Transactions on Parallel andDistri-buted Systems,2015,26(4):974-983.
[7]CHEN X,JIAO L,LI W,et al.Efficient multi-user computation offloading for mobile-edge cloud computing [J].IEEE/ACM Transactions on Networking,2016(5):2795-2808.
[8]JIA M,CAO J,YANG L.Heuristic offloading of concurrenttasks for computation-intensive applications in mobile cloud computing [C]//Proceedings of the IEEE Conference on Computer Communications Workshops.2014:352-357.
[9]YU B W,PU L J,XIE Y L,et al.Joint Task Offloading and Base Station Association in Mobile Edge Computing [J].Journal of Computer Research and Development,2018,55(3):537-550.
[10]BILAL K,ERBAD A.Edge computing for interactive media and video streaming[C]//Proceedings of the Second International Conference on Fog and Mobile Edge Computing (FMEC).2017:68-73.
[11]LONG C,CAO Y,JIANG T,et al.Edge computing framework for cooperative video processing in multimedia IoT systems [J].IEEE Transactions on Multimedia,2018,20(5):1126-1139.
[12]BAILAS C,MARSDEN M,ZHANG D,et al.Performance of video processing at the edge for crowd-monitoring applications [C]//Proceedings of the 4th World Forum on Internet of Things (WF-IoT).2018:482-487.
[13]TRAN T X,PANDEY P,HAJISAMI A,et al.Collaborative multi-bitrate video caching and processing in mobile-edge computing networks[C]//Proceedings of the 13thAnnual Confe-rence on Wireless On-demand Network Systems and Services (WONS).2017:165-172.
[14]JIANG W,CHEN G,YANG F,et al.Self-adaptive Image Sca-ling Algorithm Based on Sobel Operator [J].Computer Engineering,2010,36(7):214-216.
[15]WANG D,ZHAO H W,DAI Y,et al.A face detection acceleration algorithm based on regression [J].JournalofChongqingUniversityofPostandTelecommunications(NaturalScienceEdition),2019,31(4):550-555.
[16]SHI W S,SUN H,CHEN Y M.Prospect of new video surveillance system based on edge computing [J].China Academic Journal Electronic Publishing House,2018,35(12):60-63.
[17]GE C,BAI G W,SHEN H,et al.Edge computing-based video surveillance framework [J].Computer Engineering and Design,2019,40(1):32-39.
[18]MANGIANTE S,KLAS G,NAVON A,et al.VR is on theedge:How to deliver 360 videos in mobile networks[C]//Proceedings of the Workshop on Virtual Reality and Augmented Reality Network.2017:30-35.
[19]HU P,NING H,QIU T,et al.Fog computing based face identification and resolution scheme in Internet of Things [J].IEEE Transactions on Industrial Informatics,2017,13(4):1910-1920.
[20]ZHANG Q,ZHANG Q,SHI W,et al.Distributed collaborative execution on the edges and its application to AMBER alerts [J].IEEE Internet of Things Journal,2018,5(5):3580-3593.
[21]ZHANG Z,ZHANG X Q,ZUO D C,et al.Research on Target Tracking Application Deployment Strategy for Edge Computing [J].Journal of Software,2020,31(9):2691-2708.
[22]SUN H,YU Y,SHA K,et al.mVideo:Edge Computing Based Mobile Video Processing Systems [J].IEEE Access,2019,8:11615-11623.
[23]LONG C C,CAO Y,JIANG T,et al.Edge Computing Framework for Cooperative Video Processing in Multimedia IoT Systems [J].IEEE Transactions on Multimedia,2017,20(5):1126-1139.
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