Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

A Motion Correspondence Algorithm based on Point Series Similarity

점 계열 유사도에 기반한 모션 대응 알고리즘

  • 엄기열 (성균관대학교 정보통신공학부) ;
  • 정재영 (동양대학교 컴퓨터정보전학과) ;
  • 김문현 (성균관대학교 정보통신공학부)
  • Received : 2009.11.19
  • Accepted : 2010.02.13
  • Published : 2010.04.15
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose a heuristic algorithm for motion correspondence based on a point series similarity. A point series is a sequence of points which are sorted in the ascending order of their x-coordinate values. The proposed algorithm clusters the points of a previous frame based on their local adjacency. For each group, we construct several potential point series by permuting the points in it, each of which is compared to the point series of the following frame in order to match the set of points through their similarity based on a proximity constraint. The longest common subsequence between two point series is used as global information to resolve the local ambiguity. Experimental results show an accuracy of more than 90% on two image sequences from the PETS 2009 and the CAVIAR data sets.

본 논문에서는 점 계열 유사도에 기반하여 모션 대응에 대한 휴리스틱 알고리즘을 제안한다. 점 계열은 x좌표를 기준으로 하여 오름 차순으로 정렬된 일련의 점 들의 리스트이다. 본 연구에서는 국부근접성에 기반하여 현재 프레임 전 프레임의 점들을 클러스터링 하고, 각 그룹에 있는 점 들의 순서를 변경해 가면서 여러 개의 가상 점 계열들을 구성하며, 가상 점 계열은 근접 제약조건에 근거한 유사도를 이용하여 현재 프레임의 점 계열과 정합한다. 국부적 애매함을 해결하기 위해 두 개 점 계열간의 가장 긴부분 문자열을 찾는 방법이 전체적인 정보로 사용된다. PETS2009과 CAVIAR데이터 집합들과 같은 다양한 영상 이미지 시퀀스에 대해 본 연구의 모션 대응 알고리즘은 90%이상의 정확도를 보여준다.

Keywords

References

  1. J. Y. Jung, M. H. Kim, "Automatic feature point extraction and tracking in image sequences with the multiple moving objects," Journal of KIISE, vol.25, no.3, pp.562-573, Mar. 1998. (in Korean)
  2. A. Yilmaz, O. Javed, and M. Shah, "Object tracking: a survey," ACM Computing Surveys, vol.38, no.4, pp.1-45, 2006.
  3. J. H. Kao, J. H. Chuang, M. L. Hsieh, "Finding point correspondence using local similarity and global constraint under insignificant scaling and roll," Electronics Letters, vol.42, no.23, pp. 1340- 1341, Nov. 2006. https://doi.org/10.1049/el:20062848
  4. C. J. Veenman, M. J. T. Reinders, E. Backer, "Resolving motion correspondence for densely moving points," IEEE Trans. Pattern Anal. Mach. Intell., vol.23, no.1, pp.54-72, Jan. 2001. https://doi.org/10.1109/34.899946
  5. I. Sethi, R. Jain, "Finding Trajectories of Feature Points in a Monocular Image Sequence," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.9, no.1, pp.56-73, Jan. 1987.
  6. K. Rangarajan, M. Shah, "Establishing motion correspondence," Conference Vision Graphics Image Process, vol.54, no.1, pp.56-73, July 1991.
  7. K. Shafique, M. Shah, "A Noniterative Greedy Algorithm for Multiframe Point Correspondence," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.27, no.1, pp.51-65, Jan. 2005. https://doi.org/10.1109/TPAMI.2005.1
  8. M.D. Morse, J. M. Patel, "An Efficient and accurate method for evaluating time series similarity," In the ACM SIGMOD Int. Conf. on Management of data, pp.569-580, 2007.
  9. L. Chen, M. T. Ozsu, and V. Oria, "Robust and fast similarity search for moving object trajectories," ACM, pp.491-502, 2005.
  10. CAVIAR project, http://homepages.inf.ed.ac.uk/rbf/ CAVIAR
  11. PETS 2009, http://www.cvg.rdg.ac.uk/PETS2009/
  12. F. Yin, D. Makris, S. Velastin, "Performance Evaluation of Object Tracking Algorithms," In 10th IEEE International Workshop on PETS2007, pp. 17-24, Oct. 2007.