DOI QR코드

DOI QR Code

칼만 필터와 가변적 탐색 윈도우 기법을 적용한 강인한 이동 물체 추적 알고리즘

Robust Tracking Algorithm for Moving Object using Kalman Filter and Variable Search Window Technique

  • 김영군 (서경대학교 전자공학과) ;
  • 현병용 (서경대학교 전자공학과) ;
  • 조영완 (서경대학교 컴퓨터공학과) ;
  • 서기성 (서경대학교 전자공학과)
  • 투고 : 2011.01.05
  • 심사 : 2012.05.25
  • 발행 : 2012.07.01

초록

This paper introduces robust tracking algorithm for fast and erratic moving object. CAMSHIFT algorithm has less computation and efficient performance for object tracking. However, the method fails to track a object if it moves out of search window by fast velocity and/or large movement. The size of the search window in CAMSHIFT algorithm should be selected manually also. To solve these problems, we propose an efficient prediction technique for fast movement of object using Kalman Filter with automatic initial setting and variable configuration technique for search window. The proposed method is compared to the traditional CAMSHIFT algorithm for searching and tracking performance of objects on test image frames.

키워드

참고문헌

  1. N. D. Binh, "A robust framework for visual object tracking," International Conf. on Computing and Communication Technologies, pp. 1-8, 2009.
  2. L. Rui, D. Zhijiang, H. Fujun, K. Minxiu, and S. Lining, "Tracking a moving object with mobile robot based on vision," International Conf. on Neural Networks (IJCNN '08), pp. 716-720, 2008.
  3. T. Jin and H. Tack, "A study on kohenen network based on path determination for efficient moving trajectory on mobile robot," International Journal of Fuzzy Logic and Intelligent Systems, vol. 10, no. 2, pp. 101-106, 2010. https://doi.org/10.5391/IJFIS.2010.10.2.101
  4. D. Lee, H. Jeon, and Y. Joo, "Collaborative tracking algorithm for intelligent video surveillance systems using multiple network cameras," Journal of Korean Institute of Intelligent Systems (in Korean), vol. 21, no. 6, pp. 743-748, 2011. https://doi.org/10.5391/JKIIS.2011.21.6.743
  5. L. Davis, V. Philomin, and R. Duraiswami, "Tracking humans from a moving platform," International Conf. on Pattern Recognition, vol. 4, pp. 171-178, 2000.
  6. H. Zhou, Y. Yuan, and C. Shi, "Object tracking using SIFT features and mean shift," Computer Vision and Image Understanding, vol. 113, no. 3, pp. 345-352, 2009. https://doi.org/10.1016/j.cviu.2008.08.006
  7. D. Comaniciu, V. Ramesh, and P. Meer, "Real-time tracking of non-rigid objects using mean shift," IEEE Conf. on Computer Vision and Pattern Recognition (CVPR'00), pp. 2142-2151, 2000.
  8. G. R. Bradski, "Computer vision face tracking for use in a perceptual user interface," Intel Technology Journal, Q2, 1998.
  9. P. Vadakkepat, P. Lim, L. C. De silva, L. jing, and L. L. Ling, "Multimodal approach to human-face detection and tracking," IEEE Trans. on Industrial Electronics, vol. 55, no. 3, pp. 1385-1393, 2008. https://doi.org/10.1109/TIE.2007.903993
  10. D. Exner, E. Bruns, D. Kurz, A. Grundhofer, and O. Bimber, "Fast and robust CAMShift tracking," IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops, pp. 9-16, 2010.
  11. G. Bishop and G. Welch, "An introduction to the Kalman filter," SIGGRAPH, Course 8, 2001.
  12. X. Gang, C. Yong, C. Jiu-jin, and G. Fei, "Automatic camshift tracking algorithm based on fuzzy inference background difference combining with twice seraching," International Conf. on E-Health Networking, Digital Ecosystems and Technologies, pp. 1-4, 2010.
  13. C. Zhang, Y. Qiao, E. Fallon, and C. Xu, "An improved camshift algorithm for target tracking in video surveillance," Conf. of 9th. Information Technology & Telecommunication, pp. 19-26, 2009.
  14. Y. Ling, J. Zhang, and J. Xing, "Video object tracking based on position prediction guide CAMSHIFT," 3rd International Conf. on Advanced Computer Theory and Engineering, pp. 159-164, 2010.
  15. Intel Open Source Computer Vision Library. http://opencv.willow-garage.com/wiki/

피인용 문헌

  1. GPU Accelerating Methods for Pease FFT Processing vol.20, pp.1, 2014, https://doi.org/10.5302/J.ICROS.2014.13.1960