한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제23권1호
  • /
  • Pages.41-48
  • /
  • 2018
  • /
  • 1598-849X(pISSN)
  • /
  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
권호 표지
DOI QR코드

DOI QR Code

Study on a Robust Object Tracking Algorithm Based on Improved SURF Method with CamShift

  • Ahn, Hyochang (Dept. of Smart & PhotoVoltaic Convergence, Far East University) ;
  • Shin, In-Kyoung (Dept. of Applied Computer Engineering, Dankook University)
  • 투고 : 2017.12.15
  • 심사 : 2018.01.23
  • 발행 : 2018.01.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Recently, surveillance systems are widely used, and one of the key technologies in this surveillance system is to recognize and track objects. In order to track a moving object robustly and efficiently in a complex environment, it is necessary to extract the feature points in the interesting object and to track the object using the feature points. In this paper, we propose a method to track interesting objects in real time by eliminating unnecessary information from objects, generating feature point descriptors using only key feature points, and reducing computational complexity for object recognition. Experimental results show that the proposed method is faster and more robust than conventional methods, and can accurately track objects in various environments.

키워드

참고문헌

  1. J. Pons, J. Prades-Nebot, A. Albiol and J. Molina, "Fast motion detection in compressed domain for video surveillance.", Electronics letters Vol. 38, No. 9, pp. 409-411, 2002. https://doi.org/10.1049/el:20020290
  2. M. Tiwari and R. Singhai, "Improved Algorithm for Object Tracking in Video Camera Network", Indian Journal of Science and Technology, Vol. 10, No. 10, pp. 1-10, 2017.
  3. M. M. Bhajibhakare and P. K. Deshmukh, "Detection and tracking of moving object for surveillance system.", International Journal of Application of Innovation in Engineering and Management Vol. 2, No. 12, pp. 298-301, 2013.
  4. Q. Wang and Z. Gao, "Study on a Real-time Image Object Tracking System", International Symposium on Computer Science and Computational Technology, Vol. 2, pp. 788-791, 2008.
  5. Z. Zhou, D. We, X. Peng, Z. Zhu and K. Luo, "Object Tracking Based on Camshift with Multi-feature Fusion", Journal of Software, Vol. 9, No. 1, pp. 147-153, 2014.
  6. A. Yilmaz, O. Javed and M. Shah "Object Tracking: A survey", ACM Computing Surveys, Vol. 38, No. 4, Article. 13, 2006.
  7. K. Werner, M. Kampel, "Interest point based tracking", International Conference on Pattern Recognition, pp. 3549-3552, 2010.
  8. T. M. Barroso and P. F. Whelan, "Enhancing SURF Feature Matching Using Colour Histograms", Irish Machine Vision and Image Processing Conf., pp. 111-112. 2011.
  9. M. Du, J. Wang, J. Li, H. Cao, G. Cui, J. Lv and X. Chen, "Robot Robust Object Recognition based on Fast SURF Feature Matching", Chinese Automation Congress, pp. 581-586, 2013.
  10. Y. Kim, W. Han, Y. H. Lee, C. G. Kim, and K. J. Kim, "Object Tracking and Recognition Based on Reliability Assessment of Learning in Mobile Environments". Wireless Personal Communications, Vol. 94, No. 2, pp. 267-282, 2017. https://doi.org/10.1007/s11277-016-3292-y
  11. K. Du, Y. Ju, Y. Jin, G. Li, Y. Li and S. Qian, "Object Tracking based on Improved MeanShift and SIFT", 2nd Int. Conf. on Consumer Electronics, pp. 2716-2719, 2012.
  12. J. Y. Zhang, H. Y. Wu, S. Chen and D. S. Xia, "The Target Tracking Method Based on Camshift Algorithm Combined with SIFT", Advanced Materials Research, Vol. 186, pp. 281-286, 2011. https://doi.org/10.4028/www.scientific.net/AMR.186.281
  13. H. Ahn and Y. Lee, "Performance analysis of object recognition and tracking for the use of surveillance system", Journal of Ambient Intelligence and Humanized Computing Vol. 7, No. 5, pp. 673-679, 2016. https://doi.org/10.1007/s12652-015-0325-4
  14. S. A. Dave, M. S. Nagmode and A. Jahagirdar, "Statistical Survey on Object Detection and Tracking Methodologies", International Journal of Scientific and Engineering Research, Vol. 4, Issue. 3, 2013.
  15. D. Comaniciu, V. Ramesh and P. Meer, "Kernel-Based Object Tracking", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 5, pp. 564-577, 2003. https://doi.org/10.1109/TPAMI.2003.1195991
  16. I. Leichter, M. Lindenbaum and E. Rivlin, "Mean Shift tracking with multiple reference color histograms", Computer Vision and Image Understanding, Vol. 114, No. 3, pp. 400-408, 2010. https://doi.org/10.1016/j.cviu.2009.12.006
  17. J. Wang, F. He, X. Zhang and Y. Gao, "Tracking Objects through Occlusions Using Improved Kalman Filter", International Conference on Advanced Computer Control, pp. 23-228, 2010
  18. Y. Yue, Y. Gao and X. Zhang, "An Improved Camshift Algorithm Based on Dynamic Background", International Conference on Information Science and Engineering, pp. 1141-1144, 2009.
  19. L. Juan and O. Gwun, "A Comparison of SIFT, PCA-SIFT and SURF", International Journal of Image Processing, Vol. 3, Issue. 4, pp. 143-152, 2009.
  20. S. Ha and Y. Moon, "Multiple Object Tracking Using SIFT Features and Location Matching", International Journal of Smart Home, Vol. 5, No. 4, pp. 17-26, 2011.
  21. Y. Ke and R. Sukthankar, "PCA-SIFT: A More Distinctive Representation for Local Image Descriptors", Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 506-513, 2004.
  22. D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints", International Journal of Computer Vision, Vol. 60, Issue. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  23. M. Brown and D. Lowe, "Invariant Features from Interest Point Groups", British Machine Vision Conference, pp. 656-665, 2002.
  24. M. Stommel and O. Herzog, "Binarising SIFT-Descriptors to Reduce the Curse of Dimensionality in Histogram-Based Object Recognition", International Journal of Signal Processing, Image Processing and Pattern Recognition, pp. 320-327, 2009.
  25. H. Bay, A. Ess, T. Tuytelaars and L. V. Gool, "Speeded-Up Robust Features (SURF)", Computer Vision and Image Understanding, Vol. 110, Issue. 3, pp. 346-359, 2008. https://doi.org/10.1016/j.cviu.2007.09.014
  26. P. Viola and M. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features", Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 511-518, 2001.
  27. D. Exner, E. Bruns, D. Kurz and A. Grundhofer, "Fast and Robust CAMShift Tracking", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 9-16, 2010.
  28. N. Q. Nguyen, S. F. Su, Q. V. Tran, V. T. Nguyen, and J. T. Jeng, "Real time human tracking using improved CAM-shift" In Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS), 2017 Joint 17th World Congress of International, IEEE. pp. 1-5, 2017.
  29. R. Stolkin, I. Florescu and G. Kamberov, "An Adaptive Background Model for Camshift with a Moving Camera", 6th Int. Conf. on Advances in Pattern Recognition, pp. 261-265, 2007.
  30. A. Basit, M. N. Dailey, P. Laksanacharoen, and J. Moonrinta, "Fast Target Redetection for CAMSHIFT using Back-projection and Histogram Matching", Int. Conf. on Computer Vision Theory and Applications, Vol. 3, pp. 507-514, 2014.
  31. X. Liu, H. Chu and P. Li, "Research of the Improved Camshift Tracking Algorithm", Int. Conf. on Mechatronics and Automation, pp. 968-972, 2007.
  32. A. R. J. Francois, "CAMSHIFT Tracker Design Experiments with Intel OpenCV and SAI", International Conference on Pattern Recognition, No.4, pp. 1-4, 2004.
  33. https://sites.google.com/site/jingjingmengsite/