期刊文献+

基于人脸识别和无线射频识别的行人移动轨迹监控系统及实现 被引量:3

Pedestrian Movement Monitoring System and Implementation Based on Face Recognition and RFID Recognition
下载PDF
导出
摘要 为了实现了目标人物全区域无死角的移动轨迹跟踪,本文采用室外高清摄像头进行人脸识别和室内无线射频装备进行射频识别,将人、物、事件三者联系在一起.在保证了人物移动信息完整的基础上,提供二维全景地图轨迹浏览、指定区域视频片段预览、指定用户视频搜索等更丰富的交互方式,为分析目标运动轨迹和行为异常情况提供了更多更全面的手段.实验结果表明,本文采用的方法在移动南方基地视频数据测试集中能取得满意的识别效果. In order to achieve object trajectory tracking without blind angle, we propose a pedestrian movement monitoring system with face recognition outdoor and RFID radio frequency equipment indoor, which connect people, object and events. While granting integrated human movement information, we also provide two- dimensional trajectory panoramic map browsing, preview video clips on designated area, video search specified users for object motion trajectory and behavioral abnormalities. The experimental results show that the proposed system method can obtain satisfactory identification result on video test set of china mobile southern base.
出处 《广东技术师范学院学报》 2015年第11期21-25,共5页 Journal of Guangdong Polytechnic Normal University
基金 广东省自然科学基金-博士启动(2015A030310340) 广州市科技计划项目(软科学专项201510020013) 广东省高等学校优秀青年教师培养计划(Yq2013108)
关键词 人脸识别 无线射频识别 轨迹跟踪 face recognition RFID trajectory tracking
  • 相关文献

参考文献12

  • 1Li S Z,Chu R,Liao S,et al. Illumination invariant face recognition using n-ear infrared images [J].IEEE Transaction on Pattern Analysis and Machine Intel-ligence. 2007,29 ( 4 ) : 627- 639.
  • 2Tan Xiaoyang, Triggs B. Enhanced Local Texture Fea- ture Sets for Face Recognition Under Difficult Lighting Conditions [J]. IEEE Transactions on Image Processing, 2010, 19(6): 1635-1650.
  • 3Z. Liu and C. Liu, "Fusion of color, local spatial and global frequency information for face recognition," Pattern Recognit. , vol. 43, no. 8, pp.2882 2890, Aug. 2010.
  • 4Marszalek M. Local Features and Kernels for Classifi- cation of Texture and Object Categories: A Compre- hensive Study [J ]. International Journal of Computer Vision, 2007, 73(2): 213-238.
  • 5Rodriguez Y,Marcel S. Face authentication using adapted local binary pattern histograms [J]. ECCV,2006, (4) : 321- 332.
  • 6P. H. Hennings-Yeomans, B. V. K. Vijaya Kumar, and S. Baker, Robust low-resolution face identification and verification using high-resolution features [C].// Proceedings of IEEE ICIP, 2009:33-36.
  • 7Zhu Feng, Zhang Xianda, Hu Ya-feng. Gabor filter approach to joint feature extraction and target recogni-tion[J]. IEEE transactions on aerospace and electr-on- ic systems,2009,45 ( 1 ): 17-29.
  • 8C. Jones and A. L. Abbott, " Color face recognition by hypercomplex gabor analysis," in Proc. IEEE Int. Conf. Autom. FGR, 2006, p. 131.
  • 9李盛文,鲍苏苏.基于PCA+AdaBoost算法的人脸识别技术[J].计算机工程与应用,2010,46(4):170-173. 被引量:30
  • 10T. Ahonen, A. Hadid, and M. Pietikainen. Face description with local binary pattern: Application to face recognition [J]. IEEE Trans. Pattern Anal. Maeh. Intell., 2006, 28 ( 12 ) :2037-2041.

二级参考文献12

  • 1Kirby M,Sirovich L.Application of the KL procedure for the characterization of human faces[J].IEEE Tran Pattern Anal Machine Intell, 1990,12( 1 ) : 103-108.
  • 2Turk M,Pentland A.Eigenfaces for recognition[J].J Cognitive Neuroscience, 1991,3 ( 1 ) : 71-86.
  • 3Turk M,Pentland A.Face recognition using eigenfaces[C]//Proceeding of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994:586-591.
  • 4Pentland A.View-based modular eigenspaces for face recognition[C]// Proc IEEE Conf on CVPR,1994:84-91.
  • 5Yambor W,Draper B,Beveridge J R.Analysis of PCA-based face recognition algorithms : Eigenvector selection and distance measures[C]//Second Workshop on Empirical Evaluation Methods in Computer Vision, 2000.
  • 6Paul V,Michael J.Rapid object detection using a Boosted cascade of simple features[C]//Proc IEEE Conf on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, 2001.
  • 7Hansen L K,Salamon P.Neural network ensembles[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,1990,12(10): 993-1001.
  • 8Schapire R E,Freund Y,Bartlett Y,et al.Boosting the margin:A new explanation for the effectiveness of voting methods[J].The Annuals of Statistics, 1998,26(5 ) : 1651-1686.
  • 9Pentland A,Starner T,Etcoff N,et al.Experiments with eigenfaces[J]. IEEE Trans Pattern Anal Machine Intell,2004,26(5):572-581.
  • 10Freund Y,Schapire R E.A decision-theoretic generalization of on line learning and an application to Boosting [J].Joumal of Computer and System Sciences, 1997(55) : 119-139.

共引文献34

同被引文献15

引证文献3

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部