DOI QR코드

DOI QR Code

Experimentation and Evaluation of Energy Corrected Snake(ECS) Algorithm for Detection and Tracking the Moving Object

이동물체 탐지 및 추적을 위한 에너지 보정 스네이크(ECS) 알고리즘의 실험 및 평가

  • 양성실 (국방대학교 전산정보학과) ;
  • 윤희병 (국방대학교 전산정보학과)
  • Published : 2009.08.31

Abstract

Active Contour Model, that is, Snake algorithm is effective for detection and tracking the objects. However, this algorithm has some drawbacks; numerous parameters must be designed(weighting factors, iteration steps, etc.), a reasonable initialization must be available and moreover suffers from numerical instability. Therefore we propose a novel Energy Corrected Snake(ECS) algorithm which improved on external energy of Snake algorithm for detection and tracking the moving object more effectively. The proposed algorithm uses the difference image, getting when the object is moving. It copies four direction images from the difference image and performs the accumulating compute to erasing image noise, so that it gets external energy steadily. Then external energy united with contour that is computed by internal energy. Consequently we can detect and track the moving object more speedily and easily. To show the effectiveness of the proposed algorithm, we experiment on 3 situations. The experimental results showed that the proposed algorithm outperformed by 6$\sim$9% of detection rate and 6$\sim$11% of tracker detection rate compared with the Snake algorithm.

능동 윤곽선 모델, 즉 스네이크 알고리즘은 물체 탐지 및 추적에 사용되는 유용한 알고리즘이다. 그러나 이 알고리즘은 요소별 가중치 부여 및 반복단계 시 많은 변수가 필요하고, 초기화 애로 및 계산상 불안정성 등의 단점이 있다. 따라서 본 논문에서는 이러한 단점을 개선하여 보다 효과적인 이동물체 탐지 및 추적을 위해 기존 스네이크 알고리즘의 외부 에너지를 개선한 새로운 에너지 보정 스네이크(ECS) 알고리즘을 제안한다. 이를 위해 이동물체 이동 시 획득한 차영상 이미지를 4개의 방향성 이미지로 복사하고 각 이미지 픽셀에 대해 누적 연산 후 에너지 강화배열 내 저장 및 노이즈 제거를 통해 안정적인 이미지, 즉 외부 에너지를 획득한다. 또한 별도로 계산된 내부 에너지를 통해 얻어진 윤곽선(contour)을 외부 에너지에 병합함으로써 빠르고 쉬운 이동물체 탐지 및 추적이 가능하다. 제안한 알고리즘의 효용성을 확인하기 위해 3가지 상황을 대상으로 실험하였다. 실험 결과, 제안한 알고리즘이 기존 스네이크 알고리즘에 비해 탐지율은 평균 6$\sim$9%, 추적율은 6$\sim$11% 정도의 향상을 보였다.

Keywords

References

  1. Program Manager FCS, 'Future Combat Systems (Brigade Combat Team(FCS(BCT)) 14+1+1 Systems Overview,' 2007
  2. http://www.dodaam.com/product/c4i/aegis.html
  3. M. Kass, A. Witkin and D. Terzopoulos, 'Snake: Active Contour Models,' Int. Journal of CV, Vol.1, pp.321-331, 1988 https://doi.org/10.1007/BF00133570
  4. L. D. Cohena and I. Cohen, 'Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3- D Images,' IEEE Transactions on PAMI, Vol.15, No.11, pp.1131-1147, 1993 https://doi.org/10.1109/34.244675
  5. D. J. Williams and M. Shah, 'A Fast Algorithm for Active Contours and Curvature Estimation,' CVGIP: Image Understanding, Vol.55, No.1, pp.14-26, 1992 https://doi.org/10.1016/1049-9660(92)90003-L
  6. C. Y. Xu and J. L. Prince, 'Snakes, Shapes, and Gradient Vector Flow,' IEEE Transaction on IP, Vol.7, No.3, pp.359-369, 1998 https://doi.org/10.1109/83.661186
  7. N. Xu and N. Ahuja, 'Object Contour tracking Using Graph Cuts Based Active contour,' Int. Conference on IP, Vol.3, pp.277-280, 2002
  8. K. H. Seo, J. H. Shin, W. Kim, and J. J. Lee, 'Real- time Object Tracking and Segmentation Using Adaptive Color Snake Model,' Int. Journal of CAS, Vol.4, pp.236-246, 2006
  9. J. J. Choi and J. S. Kim, 'Modified energy function of the active contour model for the tracking of deformable objects,' Int. Journal of PEM, Vol.7, No.1, pp.47-50, 2006
  10. A. I. Comport, E. Marchand and F. Chaumette, 'Robust model-based tracking for robot vision,' Int. Conference on IROS, Vol.1, pp.692-697, 2004 https://doi.org/10.1109/IROS.2004.1389433
  11. M. Cazorla and F. Escolano, 'Feature Extraction and Grouping for Robot Vision Tasks,' Cutting Edge Robotics, pp.91-104, 2005
  12. M. Sonka, V. Hlavac and R. Boyle, 'Image Processing Analysis, and Machine Vision', 3rd ED., Thomson, 2008
  13. M. Yokoyama and T. Poggio, 'A contour-Based Moving Object Detection and Tracking," Int. Work on VSPE of TS, pp.271-276, 2005 https://doi.org/10.1109/VSPETS.2005.1570925
  14. Matrox, 'MIL/MIL-Lite version 7.0 Board-Specific Notes,' Manual No.10515-801-0700, 2001
  15. Y. G. Kim, J. S. Kim and J. Kim, 'Haptic Rendering based on Real-time Video of Deformable Bodies using Snakes Algorithm,' Int. Conference on HCI, 2007
  16. D. J. Williams and M. Shah, 'A Fast Algorithm for Active Contours and Curvature Estimation,' CVGIP: Image Understanding, Vol.55, No.1, pp.14-26, 1992 https://doi.org/10.1016/1049-9660(92)90003-L
  17. L. D. Cohen and I. Cohen, 'Finite-Element Methods for Active Contour Models and Ballons for 2-D and 3-D Images,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.15, No.11, pp.1131-1147, 1993 https://doi.org/10.1109/34.244675
  18. C. Y. Xu and J. L. Prince, 'Gradient Vector Flow: A New External Force for Snakes,' Computer Vision and Pattern Recognition Conference, pp.66-71, 1997 https://doi.org/10.1109/CVPR.1997.609299
  19. H. M. Kim, 'Implementation and Evaluation of Real- Time Moving Object Tracking System for Visual Surveillance', MS Thesis, KNDU, 2006
  20. http://www.intel.com/technology/computing/opencv/index.htm
  21. http://www.hyvision.co.kr/korea/s02/s2026.asp
  22. F. Bashir and F. Porikli, 'Performance Evaluation of Object Detection and Tracking Systems," Int. Work on PETS, pp.7-14, 2006 https://doi.org/10.1007/11612704_16
  23. S. Wu, V. K. Singh and R. Nevatia, 'Evaluation of USC Human Tracking System for Surveillance Videos,' Int. Evaluation Work on CLEAR, pp.191-196, 2007 https://doi.org/10.1007/978-3-540-68585-2_16

Cited by

  1. A Fast Snake Algorithm for Tracking Multiple Objects vol.7, pp.3, 2011, https://doi.org/10.3745/JIPS.2011.7.3.519