박사

주성분분석과 군집화를 이용한 방향별 이동 보행자 계수

김규진 2016년
논문상세정보
' 주성분분석과 군집화를 이용한 방향별 이동 보행자 계수' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 광류
  • 군집화
  • 기계학습
  • 보행자 계수
  • 주성분분석
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
1,413 0

0.0%

' 주성분분석과 군집화를 이용한 방향별 이동 보행자 계수' 의 참고문헌

  • 미국 법무성 사법연구소(NIJ)
    Buyer Beware Vol.10/11 [2002]
  • “지능형 영상보안과 휴먼인식 기술,”
    유장희 모바일 바이오인식 신융합기술 표 준연구회 [2012]
  • “인공지능,”
    김문현 생능출판사 [2001]
  • www.ilids.co.uk
  • http://imagelab.ing.unimore.it/visor/video_categories.asp
  • http://groups.inf.ed.ac.uk/vision/BEHAVEDATA/INTERACTIONS
  • Z. Sun, G. Bebis, X. Yuan, and S. Louis, “Genetic feature subset selection for gender classification: a comparison study,” IEEE Workshop on Applications of Computer Vision, Dec. 2002.
  • X. Yang, H. Liu, L. J. Latecki, “Contour-based Object Detection as Dominant Set Computation,” Pattern Recognition, vol. 45, no. 5, pp. 1927-1936, May 2012.
  • X. Gao, T. E. Boult, F. Coetzee and V. Ramesh, “Error analysis of background adaption,” IEEE International Conference on Computer Vision and Pattern Recognition, pp. 503-510, 2000.
  • W.-S. Lee, H.-H. Kim, and Y.-G. Cho, “Passenger Monitoring Method Using Optical Flow and Difference Image,” In Proc. of the Fall Conference of the Korean Society for Railway, pp. 1966-1972, Oct. 2010.
  • W. Forstner and E. Gulch, “A fast operator for detection and precise location of distinct points, corners and centres of circular features,” ISPRS Intercommission Conference on Fast Proceeding of Photogrammetric Data, pp. 281-305, 1987.
  • T. Zhao, R. Nevatia, and B. Wu, “Segmentation and tracking of multiple humans in crowded environments,” IEEE Trans. on Pattern Anal. Mach. Intell., vol. 30, no. 7, pp. 1198-1211, Jul. 2008.
  • T. Zhao and R. Nevatia, “Bayesian human segmentation in crowded situations,” Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 459-466, 2003.
  • S.-F. Lin, J.-Y. Chen, and H.-X. Chao, “Estimation of number of people in crowded scenes using perspective transformation,” IEEE Trans. Systems, Man, and Cybernetics, vol. 31, no. 6, pp. 645-654, 2001.
  • S. Pal, “Improved design of a Piecewise Linear Network,” THE UNIVERSITY OF TEXAS AT ARLINGTON, 2008.
  • S. P. N. Singh, P. J. Csonka, K. J. Waldron, “Optical Flow Aided Motion Estimation for Legged Locomotion,” In Proc. of the IEEE International Conference on Intelligent Robots and Systems, pp. 1738-1743, 2006.
  • S. Fleck and W. Strasser, “Privacy Sensitive Surveillance for Assisted Living-A Smart Camera Approach,” H. Nakashima, H. Aghajan, and J.C. Augusto(Eds), Handbook of Ambient Intelligence and Smart Environments, Springer Press, pp. 985-1014, 2010.
  • S. Cho, T. W. S. Chow and C. Leung, “A Neural-Based Crowd Estimation by Hybrid Global Learning Algorithm,” IEEE Trans. on System, Man, and Cybernetics, vol. 29, no. 4, Aug. 1999.
  • S. A. Velastin, J. H. Yin, A. C. Davies, M. A. Vicencio-Silva, R. E. Allsop and A. Penn, “Automated Measurement of Crowd Density and Motion using Image Processing,” in Proc. of IEEE International Conference on Road Traffic Monitoring and Control, pp. 127-132, April 1994.
  • Robert Rae and Helge Ritter, “Recognition of Human Head Orientation Based on Artificial Neural Networks,” IEEE Trans. on Neural Networks, vol. 9, no. 2, pp. 257-265, Mar. 1998.
  • Robert M. Haralick, “Statistical and Structural Approaches to Texture”, in Proc. the IEEE, vol. 67, no. 5, pp. 786-804, May 1979.
  • R. Ma, L. Li, W. Huang and Q. Tian, “On Pixel Count Based Crowd Density Estimation for Visual Surveillance,” in Proc. of IEEE Conference Cybernetics and Intelligent Systems, vol. 1, pp. 170-173, 2004.
  • R. M. Haralick, K. Shanmugam, Its'Hak Dinstein, “Textural Features for Image Classification,” IEEE Systems, Man, and Cybernetics Society, vol. 3, no. 6, pp. 610-621, 1973.
  • R. Haralick, B. Shanugam and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern., vol.3, pp.610–621, 1973.
  • R. Duda, P. Hart, and D. Stork, “Pattern Classification,” Wiley, 2012.
  • PETS 2009, http://www.cvg.rdg.ac.uk/PETS2009
  • P. Viola, M. Jones, and D. Snow, “Detecting pedestrians using patterns of motion and appearance,” Int. J. Comput. Vis., vol. 63, no. 2, pp. 153-161, 2005.
  • P. Avishek and J. Maiti, “Development of a Hybrid Methodology for Dimensionality Reduction in Mahalanobis-Taguchi System using Mahalanobis Distance and Binary Particle Swarm Optimization,” Expert System with applications, vol. 37, no. 2, pp. 1286-1293, 2010.
  • N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection Computer Vision and Pattern Recognition,” in Proc. IEEE Int. Conf. Comput. Vis., pp. 886-893, 2005.
  • N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proc. IEEE Conf. Comput. Vis. Pattern Recog., vol. 1, pp. 886-893, 2005.
  • L. Xiaohua, S. Lansun and L. Huanqin, “Estimation of Crowd Density Based on Wavelet and Support Vector Machine,” Trans. on Institute of Measurement and Control, vol. 28, no. 3, pp. 299-308, 2006.
  • L. Dong, V. Parameswaran, V. Ramesh, and I. Zoghlami, “Fast crowd segmentation using shape indexing,” in Proc. IEEE Int. Conf. Comput. Vis., pp. 1-8, 2007.
  • K. Huang, L. Wang, T. Tan, and S. Maybank, “A Real-Time Object Detecting and Tracking System for Outdoor Night Surveillance,” Pattern Recognition, vol. 41, no. 1, pp. 432-444, Jan. 2008.
  • Jianchang Mao and Anil K. Jain, “Artificial Neural Networks for Feature Extraction and Multivariate Data Projection,” IEEE Trans. Neural Networks, vol. 6, no. 2, pp. 296-317, Mar. 1995.
  • J. WANG AND E. ADELSON, “Representing moving images with layers,” IEEE Image Process. pp. 625-638. 1994.
  • J. Shi and C. Tomasi, “Good Features to Track," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994.
  • J. Rittscher, P. H. Tu, and N. Krahnstoever, “Simultaneous estimation of segmentation and shape,” Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 486-493, 2005.
  • J. A. Hartigan, “Clustering Algorithms,” John Wiley & Sons Inc, New York, 1975.
  • I. Ulrich and I. Nourbakhsh, “Appearance based Obstacle Detection with Monocular Color Vision,” in Proc. Association for the Advancement of Artificial Intelligence, pp. 1-4, 2000.
  • Haque, M., Murshed, M. and Paul, M “On Stable Dynamic Background Generation Technique Using Gaussian Mixture Models for Robust Object Detection,” IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, pp. 41-48, 2008.
  • H. Celik, A. Hanjalic and E. A. Hendriks, “Towards a Robust Solution to People Counting,” in Proc. of International Conference of Image Processing, pp. 2401-2404, Oct. 2006.
  • H. Bay et al., “Surf: Speeded up robust features,” European Conf. Comput. Vision, vol. 1, 2006.
  • G. Taguchi and R. Jugulum, “The Mahalanobis-Taguchi Strategy: A Pattern Technology System,”, John wiley & Sons, New York, 2002.
  • G. J. Kim, T. K. An, and M. H. Kim, “Estimation of crowd density in public areas based on neural network,” KSII Trans. on Internet and Information Systems, vol. 6, no. 9, pp. 2170-2190, Sep. 2012.
  • G. G. Lee, B. S. Kim, and W. Y. Kim, “Automatic Estimation of Pedestrian Flow,” Int. Conf. on Distributed Smart Camers, pp. 291-296, 2007.
  • F. Santoro, S. Pedro, Z-H. Tan, and T. B. Moeslund, “Crowd Analysis by using Optical Flow and Density based Clustering,” 18th European Signal Processing Conference(EUSIPCO-2010), 2010.
  • F. Fukushima and N. Wake, “Handwritten Alphanumeric Character Recognition by the Neocognition,” IEEE Trans. on Neural Networks, vol. 2, no. 3, pp. 355-365, 1991.
  • E. Rosten et al., “Reak-Time Video Annotations for Augmented Reality,” Proc. Int’l Symp. Visual Comput., 2004.
  • E. Rosten et al., “Faster and better: A machine learning approach to corner detection,” IEEE Trans. Pattern Anal. Mach. Intelligence, vol. 32, pp. 105-119, 2010.
  • E. Rosten and T. Drmmond, “Machine Learning for High Speed Corner Detection,” Proc. Ninth European Conf. Comput. Vision, vol. 1, pp. 430-443, 2006.
  • D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International J. Comput. Vision, vol. 60, pp. 91-110, 2004.
  • D. Kong, D. Gray, and H. Tao, “Counting pedestrians in crowds using viewpoint invariant training,” in Proc. Brit. Mach. Vis. Conf, 2005.
  • D. Kong, D. Gray and H. Tao, “A Viewpoint Invariant Approach for Crowd Counting,” in Proc. of International Conference of Pattern Recognition, vol. 3, pp. 1187-1190, 2006.
  • C. Wren, A. Azarbayejani, T. Darrell, and A.P.Pentland, “Pfinder: real-time tracking of the human body,” IEEE Trans. on Pattern Anal. and Machine Intell., vol. 19, no. 7, pp. 780-785, 1997.
  • C. Stauffer and W.E.L. Grimson, 1999, “Adaptive background mixture models for real-time tracking,” IEEE International Conference on Computer Vision and Pattern Recognition, June 1999.
  • C. R. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, “Pfinder: Real-time Tracking of the Human Body,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 780-785, 1997.
  • Bruhn, A., Weickert, J., Feddern, C., Kohlberger, T., & Schn rr, C. “Real-time optic flow computation with variational methods,” In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg. pp. 222-229 Aug. 2003.
  • Bruhn, A., Weickert, J., & Schn rr, C. “Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods,” Int. Journal of Computer Vision, vol. 61 no. 3, pp. 211-231, 2005.
  • B. Wu and R. Nevatia, “Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors,” Proc. Int’l Conf. on Computer Vision, pp. 90-97, Oct. 2005.
  • B. K. Horn and B. G. Schunck, “Determining optical flow,” Artificial Intelligence, vol. 17, pp. 185-203, 1980.
  • B. D. Lucas and T. Kanade, “An iterative image registration technique with an application in stereo vision,” Proc. Int’l Joint Conference on Artificial Intelligence, pp. 121-130, 1981.
  • A. Materka and M. Strzelecki, “Texture analysis methods - a review,” Technical University of Lodz, Institute of Electronics, COST B11 report, 1998.
  • A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” Proceedings of the IEEE, vol. 90, no. 7, pp. 1151-1163, 2002.
  • A. C. Davies, J. H. Yin, and S. A. Velastin, “Crowd monitoring using image processing,” Electron. Comm. Eng. J., vol. 7, pp. 37-47, 1995.
  • A. B. Chan, Z. -S. J. Liang, N. Vasconcelos, “Privacy preserving crowd monitoring: Counting people without people models or tracking,” Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1-7, 2008.
  • A. Albiol, M. J. Silla, A. Albiol, and J. M. Mossi, “Video analysis using corner motion statistics,” Proc. IEEE Int’l Workshop on Performance Evaluation of Tracking and Surveillance, pp. 31-38, 2009.