A Comparison ECGM-Based Graph Matching and Enhnaced K-Means Clustering Multi-View Video Model

Authors

  • Dr. P. Sumitra  Department of Computer Science, Vivekananda College of Arts and Sciences for Women (Autonomous), Elayamapalayam, Tamilnadu, India
  • M. Senbagapriya  Department of Computer Science, Vivekananda College of Arts and Sciences for Women (Autonomous), Elayamapalayam, Tamilnadu, India

Keywords:

Image processing, Digital Image Processing, Analog Image Processing Two dimensional signals

Abstract

In this thesis a generic methodology for the semi-automatic generation of reliable position annotations for evaluating multi-camera people-trackers on large video data sets. Most of the annotation data are automatically computed, by estimating a consensus tracking result from multiple existing trackers and people detectors and classifying it as either reliable or not. A human using a simple binary decision task, a process faster than marking the correct person position, verifies a small subset of the data, composed of tracks with insufficient reliability. The proposed framework is generic and can handle additional trackers.In this thesis studied the most commonly use face edge detection techniques of Enhnaced Sobel Edge Annotation Algorithm (ESEAA). Higher-level edge detection techniques and appropriate programming tools only facilitate the process but do not make it a simple task.

References

  1. A. Milan, K. chindler, and S. Roth, "Challenges of ground truth evalua-tion of multi-target tracking," in Proc. CVPRW, Jun. 2013, pp. 735-742.
  2. H. Wu, A. C. Sankaranarayanan, and R. Chellappa, "Online empirical evaluation of tracking algorithms," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 8, pp. 1443-1458, Aug. 2010.
  3. M. Kristan et al., "The visual object tracking VOT2013 challenge results," in Proc. ICCVW, Dec. 2013, pp. 98-111.
  4. M. Kristan et al., "The visual object tracking VOT2014 challenge results," in Proc. ECCV Workshops, 2014, pp. 191-217.
  5. J. Yuen, B. Russell, C. Liu, and A. Torralba, "LabelMe video: Building a video database with human annotations," in Proc. ICCV, Sep./Oct. 2009, pp. 1451-1458.
  6. B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman, "LabelMe: A database and Web-based tool for image annotation," Int. J. Comput. Vis., vol. 77, nos. 1-3, pp. 157-173, 2008.
  7. A. Dorado, J. Calic, and E. Izquierdo, "A rule-based video annotation system," IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 5, pp. 622-633, May 2004.
  8. M. Bertozzi, A. Broggi, R. Grisleri, A. Tibaldi, and M. D. Rose, "A tool for vision based pedestrian detection performance evaluation," in Proc. IEEE Intell. Vehicles Symp., Jun. 2004, pp. 784-789.
  9. C. Spampinato, B. J. Boom, and J. He, "MTAP special issue on meth-ods and tools for ground truth collection in multimedia applications," Multimedia Tools Appl., vol. 70, no. 1, pp. 409-412, May 2014.
  10. S. Vijayanarasimhan and K. Grauman, "Active frame selection for label propagation in videos," in Proc. ECCV, 2012, pp. 496-509.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-10-31

Issue

Volume 2, Issue 5, September-October-2017

Section

Research Articles

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How to Cite

[1]
Dr. P. Sumitra, M. Senbagapriya, " A Comparison ECGM-Based Graph Matching and Enhnaced K-Means Clustering Multi-View Video Model , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.31-37, September-October-2017.