Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (4): 180-186.doi: 10.11896/jsjkx.201200116

• Computer Graphics & Multimedia • Previous Articles     Next Articles

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).

PDF (PC)

713

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

CLC Number: 

  • 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.
[1] SUN Hui-ting, FAN Yan-fang, MA Meng-xiao, CHEN Ruo-yu, CAI Ying. Dynamic Pricing-based Vehicle Collaborative Computation Offloading Scheme in VEC [J]. Computer Science, 2022, 49(9): 242-248.
[2] YU Bin, LI Xue-hua, PAN Chun-yu, LI Na. Edge-Cloud Collaborative Resource Allocation Algorithm Based on Deep Reinforcement Learning [J]. Computer Science, 2022, 49(7): 248-253.
[3] LI Meng-fei, MAO Ying-chi, TU Zi-jian, WANG Xuan, XU Shu-fang. Server-reliability Task Offloading Strategy Based on Deep Deterministic Policy Gradient [J]. Computer Science, 2022, 49(7): 271-279.
[4] YUAN Hao-nan, WANG Rui-jin, ZHENG Bo-wen, WU Bang-yan. Design and Implementation of Cross-chain Trusted EMR Sharing System Based on Fabric [J]. Computer Science, 2022, 49(6A): 490-495.
[5] FANG Tao, YANG Yang, CHEN Jia-xin. Optimization of Offloading Decisions in D2D-assisted MEC Networks [J]. Computer Science, 2022, 49(6A): 601-605.
[6] LIU Zhang-hui, ZHENG Hong-qiang, ZHANG Jian-shan, CHEN Zhe-yi. Computation Offloading and Deployment Optimization in Multi-UAV-Enabled Mobile Edge Computing Systems [J]. Computer Science, 2022, 49(6A): 619-627.
[7] XIE Wan-cheng, LI Bin, DAI Yue-yue. PPO Based Task Offloading Scheme in Aerial Reconfigurable Intelligent Surface-assisted Edge Computing [J]. Computer Science, 2022, 49(6): 3-11.
[8] ZHANG Hai-bo, ZHANG Yi-feng, LIU Kai-jian. Task Offloading,Migration and Caching Strategy in Internet of Vehicles Based on NOMA-MEC [J]. Computer Science, 2022, 49(2): 304-311.
[9] LIN Chao-wei, LIN Bing, CHEN Xing. Study on Scientific Workflow Scheduling Based on Fuzzy Theory Under Edge Environment [J]. Computer Science, 2022, 49(2): 312-320.
[10] JING Hui-yun, ZHOU Chuan, HE Xin. Security Evaluation Method for Risk of Adversarial Attack on Face Detection [J]. Computer Science, 2021, 48(7): 17-24.
[11] LIANG Jun-bin, ZHANG Hai-han, JIANG Chan, WANG Tian-shu. Research Progress of Task Offloading Based on Deep Reinforcement Learning in Mobile Edge Computing [J]. Computer Science, 2021, 48(7): 316-323.
[12] QIAN Ji-de, XIONG Ren-he, WANG Qian-lei, DU Dong, WANG Zai-jun, QIAN Ji-ye. Application of Edge Computing in Flight Training [J]. Computer Science, 2021, 48(6A): 603-607.
[13] QIAN Tian-tian, ZHANG Fan. Emotion Recognition System Based on Distributed Edge Computing [J]. Computer Science, 2021, 48(6A): 638-643.
[14] XUE Yan-fen, GAO Ji-mei, FAN Gui-sheng, YU Hui-qun, XU Ya-jie. Energy-aware Fault-tolerant Collaborative Task Execution Algorithm in Edge Computing [J]. Computer Science, 2021, 48(6A): 374-382.
[15] SONG Hai-ning, JIAO Jian, LIU Yong. Research on Mobile Edge Computing in Expressway [J]. Computer Science, 2021, 48(6A): 383-386.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.