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Object Tracking Based on Machine Vision and improved SVDD Algorithm

International Journal on Smart Sensing and Intelligent Systems
Volume 8 (2015): Issue 1 (January 2015)
By:
Open Access
|Mar 2015

References

  1. Wei He, “Study on the method of target feature selection in video tracking”, Tsinghua, Beijing, 2007.
    Search in Google ScholarBack to article
  2. I. Sun and M. Zhang, “Based on Kalman Filter the Mobile Manipulator Non-Calibration Free Dynamic Target Tracking Technology Research”, Proceedings of 2009 International Conference on Electronic Computer Technology, 2009, pp. 610-614.10.1109/ICECT.2009.62
    Search in Google ScholarBack to article
  3. Alper Yilmaz , Omar Javed and Mubarak Shah, “Object tracking: A survey”, ACM Computing Surveys, Vol. 38, No. 4, 2006, 1-45.10.1145/1177352.1177355
    Search in Google ScholarBack to article
  4. C. VEENMAN, M. REINDERS and E. BACKER, “Resolving motion correspondence for densely moving points”, IEEE Trans. Patt. Analy. Mach. Intell, Vol. 23, No. 1, 2001, pp. 54-72.10.1109/34.899946
    Search in Google ScholarBack to article
  5. I. SETHI and R. JAIN, “Finding trajectories of feature points in a monocular image sequence”, IEEE Trans. Patt. Analy. Mach. Intell, Vol. 9, No. 1, 1987, pp. 56-73.10.1109/TPAMI.1987.476787221869377
    Search in Google ScholarBack to article
  6. S. INTILLE, J. DAVIS, and A. BOBICK, “Real-time closed-world tracking”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997, pp. 697-703.
    Search in Google ScholarBack to article
  7. K. SHAFIQUE and M. SHAH, “A non-iterative greedy algorithm for multi-frame point correspondence”, In IEEE Intern. Conference on Comp. Vision (ICCV), 2003, pp. 110-115.10.1109/ICCV.2003.1238321
    Search in Google ScholarBack to article
  8. M. ISARD and A. BLAKE, “Condensation-conditional density propagation for visual tracking”, Int. J. Comput. Vision, Vol. 29, No. 1, 1998, pp. 5-28.10.1023/A:1008078328650
    Search in Google ScholarBack to article
  9. S. ZHOU, R. CHELLAPA and B. MOGHADAM, “Adaptive visual tracking and recognition using particle filters”, In Proceedings IEEE International Conference on Multimedia and Expo. (ICME), 2003, pp. 349-352.
    Search in Google ScholarBack to article
  10. N. VASWANI, A. ROYCHOWDHURY and R. CHELLAPPA, “Activity recognition using the dynamics of the configuration of interacting objects”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2003, pp. 633-640.
    Search in Google ScholarBack to article
  11. S. BIRCHFIELD, “Elliptical head tracking using intensity gradients and color histograms”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1998, pp. 232-237.
    Search in Google ScholarBack to article
  12. P. FIEGUTH and D. TERZOPOULOS, “Color-based tracking of heads and other mobile objects at video frame rates”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997, pp. 21-27.
    Search in Google ScholarBack to article
  13. H. SCHWEITZER, J. W. BELL and F. WU, “Very fast template matching”, In European Conference on Computer Vision (ECCV), 2002, pp. 358-372.10.1007/3-540-47979-1_24
    Search in Google ScholarBack to article
  14. D. COMANICIU, V. RAMESH and P. MEER, “Kernel-based object tracking”, IEEE Trans. Patt. Analy. Mach. Intell, Vol. 25, 2003, pp. 564-575.10.1109/TPAMI.2003.1195991
    Search in Google ScholarBack to article
  15. A. JEPSON, D. FLEET and T. ELMARAGHI, “Robust online appearance models for visual tracking”, IEEE Trans. Patt. Analy. Mach. Intell, Vol. 25, No. 10, 2003, pp. 1296-1311.10.1109/TPAMI.2003.1233903
    Search in Google ScholarBack to article
  16. M. BLACK and A. JEPSON, “Eigen-tracking: Robust matching and tracking of articulated objects using a view-based representation”, Int. J. Comput. Vis., Vol. 26, No. 1, 1998, pp. 63-84.
    Search in Google ScholarBack to article
  17. S. AVIDAN, “Support vector tracking”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2001, pp. 184-191.
    Search in Google ScholarBack to article
  18. D. HUTTENLOCHER, J. NOH and W. RUCKLIDGE, “Tracking non-rigid objects in complex scenes”, In IEEE Intern. Conference on Computer Vision (ICCV), 1993, pp. 93-101.
    Search in Google ScholarBack to article
  19. J. KANG, I. COHEN and G. MEDIONI, “Continuous tracking within and across camera streams”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2003, pp. 267-272.
    Search in Google ScholarBack to article
  20. J. KANG, I. COHEN and G. MEDIONI, “Object reacquisition using geometric invariant appearance model”, In Intern. Conference on Pattern Recognition (ICPR), 2004, pp. 759-762.
    Search in Google ScholarBack to article
  21. J. MACCORMICK and A. BLAKE, “Probabilistic exclusion and partitioned sampling for multiple object tracking”, Int. J. Comput. Vision, Vol. 39, No. 1, 2000, pp. 57-71.
    Search in Google ScholarBack to article
  22. Y. CHEN, Y. RUI, and T. HUANG, “JPDAF based HMM for real-time contour tracking”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2001, pp. 543-550.
    Search in Google ScholarBack to article
  23. A. MANSOURI, “Region tracking via level set PDES without motion computation”, IEEE Trans. Patt. Analy. Mach. Intell, Vol. 24, No, 7, 2002, pp. 947-961.10.1109/TPAMI.2002.1017621
    Search in Google ScholarBack to article
  24. D. CREMERS and C. SCHNORR, “Statistical shape knowledge in variational motion segmentation”, Image and Vision Computing, Vol. 21, 2003, pp. 77-86.10.1016/S0262-8856(02)00128-2
    Search in Google ScholarBack to article
  25. A.YILMAZ, X. LI and M. SHAH, “Contour based object tracking with occlusion handling in video acquired using mobile cameras”, IEEE Trans. Patt. Analy. Mach. Intell., Vol. 26, No. 11, 2004, pp. 1531-1536.10.1109/TPAMI.2004.96
    Search in Google ScholarBack to article
  26. N.K. Suryadevara, S.C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Engineering Applications of Artificial Intelligence, Volume 26, Issue 10, November 2013, Pages 2641-2652, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004.10.1016/j.engappai.2013.08.004
    Search in Google ScholarBack to article
  27. D. M. J. Tax and R. P. W. Duin, “Support Vector Domain Description”, Pattern Recognition Letters, Vol. 20, No. 14, 1999, pp. 1191-1199.10.1016/S0167-8655(99)00087-2
    Search in Google ScholarBack to article
  28. N. K. Suryadevara and S. C. Mukhopadhyay, “Determining Wellness Through An Ambient Assisted Living Environment”, IEEE Intelligent Systems, May/June 2014, pp. 30-37.10.1109/MIS.2014.16
    Search in Google ScholarBack to article
  29. I. W. Tsang, J. T. Kwok and P. M. Cheung, “Core vector machines: Fast SVM training on very large data sets”, Journal of Machine Learning Research, Vol. 6, 2005, pp. 363-392.
    Search in Google ScholarBack to article
  30. N.K. Suryadevara and S.C. Mukhopadhyay, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly”, IEEE Sensors Journal, Vol. 12, No. 6, June 2012, pp. 1965-1972.10.1109/JSEN.2011.2182341
    Search in Google ScholarBack to article
  31. S. Asharaf, M. N. Murty and S. K. Shevade, “Multiclass Core Vector Machine”, Proc. the 24th International Conference on Machine Learning (ICML 2007), 2007, pp. 41-48.10.1145/1273496.1273502
    Search in Google ScholarBack to article
  32. Francesco Orabona, “Better Algorithms for Selective Sampling”, Proc. the 28th International Conference on Machine Learning (ICML 2011), 2011, pp. 433-440.
    Search in Google ScholarBack to article
  33. Y. Liu, “Combining integrated sampling with SVM ensembles for learning from imbalanced datasets”, Information Processing and Management, Vol. 47, No. 4, 2011.10.1016/j.ipm.2010.11.007
    Search in Google ScholarBack to article
  34. M. Volpi, “Memory-Based Cluster Sampling for Remote Sensing Image Classification”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 8, 2012.10.1109/TGRS.2011.2179661
    Search in Google ScholarBack to article
  35. G. Sen Gupta, S.C. Mukhopadhyay and M Finnie, Wi-Fi Based Control of a Robotic Arm with Remote Vision, Proceedings of 2009 IEEE I2MTC Conference, Singapore, May 5-7, 2009, pp. 557-562.10.1109/IMTC.2009.5168512
    Search in Google ScholarBack to article
  36. Abhimanyu Das and David Kempe, “Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection”, Proc. the 28th International Conference on Machine Learning (ICML 2011), 2011, pp. 1057-1064.
    Search in Google ScholarBack to article
  37. G.Sengupta, T.A.Win, C.Messom, S.Demidenko and S.C.Mukhopadhyay, “Defect analysis of grit-blasted or spray printed surface using vision sensing technique”, Proceedings of Image and Vision Computing NZ, Nov. 26-28, 2003, Palmerston North, pp. 18-23.
    Search in Google ScholarBack to article
  38. Y. Q. Wang, Y. Li, and L. Chang, “Approximate Minimum Enclosing Ball Algorithm with Smaller Core Sets for Binary Support Vector Machine”, Proc. the 2010 Chinese Control and Decision Conference (CCDC 2010), 2010, pp. 3404-3408.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.21307/ijssis-2017-778 | Journal eISSN: 1178-5608
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Language: English
Page range: 677 - 696
Submitted on: Nov 2, 2014
Accepted on: Jan 31, 2015
Published on: Mar 1, 2015
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
Object tracking,
machine vision,
Support Vector Data Description,
one-class SVM,
classification
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2015 Yongqing Wang, Yanzhou Zhang, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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