计算机科学 ›› 2015, Vol. 42 ›› Issue (3): 266-270.doi: 10.11896/j.issn.1002-137X.2015.03.055

• 图形图像与模式识别 • 上一篇    下一篇

基于众核处理器和GPU的视频快速检测方案

杨 娟,曾苗祥,徐 晶,许 炜   

  1. 华中科技大学电子与信息工程系湖北省智能互联网技术重点实验室 武汉430074,华中科技大学电子与信息工程系湖北省智能互联网技术重点实验室 武汉430074,华中科技大学电子与信息工程系湖北省智能互联网技术重点实验室 武汉430074,华中科技大学电子与信息工程系湖北省智能互联网技术重点实验室 武汉430074
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受“十二五”国家科技支撑计划课题(2011BAK08B00)资助

Fast Video Detection Scheme Based on Multi-core Processor and GPU

YANG Juan, ZENG Miao-xiang, XU Jing and XU Wei   

  • Online:2018-11-14 Published:2018-11-14

摘要: 目前基于普通架构的视频检测速度较慢,难以满足网络视频实时监测的要求,为此提出一个基于众核处理器和图形处理单元(GPU)的视频检测方案。该方案基于众核处理器实现视频解码,基于GPU实现SURF(Speed Up Robust Features)和SVM(Support Vector Machine)的图像检测算法。与基于普通PC架构的视频检测方案相比,该方案的视频检测性能提升了10倍以上。

关键词: 视频检测,众核处理器,GPU,SURF,SVM

Abstract: At present,the speed of video detection based on general structure is very slow,and it is difficult to meet the requirement of real-time network video monitoring.This paper showed a new video detection method based on multi-core processor and graphic processing unit (GPU).This method uses multi-core processor to realize video decoding,and uses the GPU to realize the SURF (Speed Up Robust Features) and SVM (Support Vector Machines) algorithm to detect the image.Compared with video detection scheme based on general PC architecture,the performance of the method based on multi-core processor and GPU can be improved over 10 times.

Key words: Video detect,Multi-core processor,GPU,SURF,SVM

[1] Zhao G,Wang S,Wang T,et al.HSV color space and face detec-tion based objectionable image detecting[C]∥2008 Second International Conference on Future Generation Communication and Networking Symposia.2008,3:107-110
[2] Yu J J,Han S W.Skin detection for adult image identification[C]∥2014 16th International Conference on Advanced Communication Technology (ICACT).IEEE,2014:645-648
[3] Lee H,Lee S,Nam T.Implementation of high performance objectionable video classification system[C]∥The 8th International Conference Advanced Communication Technology,2006(ICACT 2006).IEEE,2006,2:4-962
[4] Kim C Y,Kwon O J,Kim W G,et al.Automatic system for filtering obscene video[C]∥10th International Conference on Advanced Communication Technology,2008(ICACT 2008).IEEE,2008,2:1435-1438
[5] Yu W,Qu Z,Jin Y.A Pornographic Video Detection MethodBased on Semi-supervised Learning on Graphs[C]∥2013 Sixth International Symposium on Computational Intelligence and Design (ISCID).IEEE,2013,2:347-350
[6] Ochoa V M T,Yayilgan S Y,Cheikh F A.Adult video content detection using Machine Learning Technology[C]∥2012 Eighth International Conference on Signal Image Technology and Internet Based System(SITIS).2012:967-974
[7] Esmaeili M M,Fatourechi M,Ward R K.A robust and fast video copy detection system using content-based fingerprinting[J].IEEE Transactions on Information Forensics and Security,2011,6(1):213-226
[8] Endeshaw T,Garcia J,Jakobsson A.Classification of indecentvideos by low complexity repetitive motion detection[C]∥37th IEEE Applied Imagery Pattern Recognition Workshop,2008(AIPR’08).IEEE,2008:1-7
[9] Wu J,Wang C F.Fast computation of cylindrical Green’s functions with graphic processing unit[C]∥Antennas and Propagation Society International Symposium (APSURSI).2013:1884-1885
[10] Mirollo A C,Guerrero J J,Sagues C.SURF features for efficient robot localization with omnidirectional image[C]∥2007 IEEE International Conference on Robotics and Automation.2007:3901-3907
[11] Szczuko P.Influence of image transformations and quality degradations on SURF detector efficiency[C]∥Signal Processing:Algorithms,Architectures,Arrangements,and Applications (SPA),2013.IEEE,2013:285-290

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