The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

The Korea Contents Association (한국콘텐츠학회)
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Multiple Objection and Tracking based on Morphological Region Merging from Real-time Video Sequences

실시간 비디오 시퀀스로부터 형태학적 영역 병합에 기반 한 다중 객체 검출 및 추적

  • Published : 2007.02.28
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Abstract

In this paper, we propose an efficient method for detecting and tracking multiple moving objects based on morphological region merging from real-time video sequences. The proposed approach consists of adaptive threshold extraction, morphological region merging and detecting and tracking of objects. Firstly, input frame is separated into moving regions and static regions using the difference of images between two consecutive frames. Secondly, objects are segmented with a reference background image and adaptive threshold values, then, the segmentation result is refined by morphological region merge algorithm. Lastly, each object segmented in a previous step is assigned a consistent identification over time, based on its spatio-temporal information. The experimental results show that a proposed method is efficient and useful in terms of real-time multiple objects detecting and tracking.

본 논문에서는 카메라로부터 획득 되어진 비디오 시퀀스로부터 다중 움직임 객체와 배경을 분할하고 시공간 정보에 기반 한 객체 추적 방법을 제안한다. 제안한 방법은 3단계로 구성되어 있다. 먼저 입력 비디오 시퀀스로부터 프레임 사이의 차를 이용한 움직임 영역과 움직임이 존재하지 않는 영역을 구분하여 적응적 경계간을 추출한다. 두 번째는 참조 배경영상과 적응적 경계값을 이용하여 움직임이 존재하는 영역으로부터 개략적 객체 분할을 수행하며, 분할된 이진영상에 형태학적 영역 병합 알고리즘을 적용하여 객체 병합을 수행하였다. 마지막으로 분할된 객체에 시공간 정보를 이용하여 객체에 임의의 ID를 할당하여 추적하였다. 카메라로부터 획득되어진 비디오 시퀀스를 이용한 실험에서 객체들의 분할 및 추적의 효율성과 시스템의 유용성을 확인하였다.

Keywords

References

  1. N. Oliver, B. Rosario, and A Pentland, "A Bayesian Computer Vision System for Modeling Human Interaction，" Int Conf. on Vision Systems, pp.255-272, 1999.
  2. C. R Wren, A Azarbayejani，T. Darrell, and A. Pentland, "TM Pinder: Realtime Tracking of the Human Body，"IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.19, No.7, pp.780-785, 1997. https://doi.org/10.1109/34.598236
  3. S. Niyogi and E. Adelson, "Ana!yzing Gait Spatiotemporal Surface," lEEE Workshop, pp.64-69, 1994.
  4. K. Sato and J. K. Aggarwal，"Tracking and Recognizing Two-person Interaction in Outdoor Image Sequences," IEEE Workshop on Multi-object， pp.87-94， 2001.
  5. M Quming and J K Aggarwal, "Tracking and Classîfying Moving Objects from Video," IEEE Intemational Workshop on PETS, 2001.
  6. A Lipton, H Fujiyoshi, and R Patil, "Moving Target Classîfying Moving Objects from Video," DARPA Image Understanding Workshop，1998.
  7. N. Paragíos and R Deriche, "Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Object," IEEE Transactions on Pattern Analysís and Machine Intelligence, Vol.22, No.3, pp.266-280, 2000. https://doi.org/10.1109/34.841758
  8. I. Haritaoglu, D. Harwood, and L. S. Davis, "Real-time Surveillance of People and Their Activities." lEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, No.3, pp.809-830, 2000. https://doi.org/10.1109/34.868683
  9. M Yokoyama and T. Poggio, "A Contour based Moving Object Detection and Tracking," IEEE Intemational Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005.
  10. L. Li, W. Huang, L. Y. H Gu, and Q. Tian, "Foreground Object Detection in Changing Background based on Color co-occurrence Statistics," IEEE Workshop on Applications of Computer Vision, 2002
  11. C. Stauffer and W. E. L. Grimson, "Adaptive Background Mixture Models for Real-time Tracking，" IEEE Conference Computer Vision and Pattern Recogniton. 1999.
  12. A. Mittal and N. Paragios, "Motion-based Background Subtraction using Adaptive Kemel Density Estimation," IEEE Conference Computer Vision and Pattem Recongnition，Vol.2, pp.302-309，2004.
  13. A Elgammal, D. Harwood, and L. Davis, "Non-pararnetric Malel for Background Subtraction," European Conference Computer Vision，Vol.ll, pp.751-767, 2000.
  14. J. L. Barron, D. J. Fleet, and S. S. Beauchemin, "Performance of optical Flow Techniques," Intemational Joumal Computer Vision, Vol. 12, No.1, pp.43-77, 1994. https://doi.org/10.1007/BF01420984
  15. J. H. Duncan and T. C. Chou, "On the Detection of Motion and the Olmputation of Optical Flow," IEEE Transactions on Pattern Analysis and l'vIachine Intelligence, Vol.14, No.3, pp.346-352, 1992 https://doi.org/10.1109/34.120329
  16. V. Caselles and B. Coll, "Snakes in Movement," SIAM Joumal Nurnerical Analysis， Vol.33, pp 2445-2456，1996. https://doi.org/10.1137/S0036142994275044
  17. S. S. Beauchemin and J L. Barron, "The Computation of Optical Flow," ACM Comuting Surveys, 1995.
  18. J Sethian, Level Methods and Fast Marching，Methods, Cambridge Unviersity. Press, 1999.
  19. L. Qiu and L. Li, "Contour Extraction of Moving Object," IEEE Intemational Conference Pattern Hecognition, Vol 2, pp.1427-1432， 1998.
  20. N. Paragios and R Deriche, "Geodesic Active Contours and Level Scts for the Detection and Tracking for Moving Objects," IEEE Transactions on Pattem Analysis and Machine Intelligence, Vol.22, No.3, pp.266-280, 2000.
  21. 김준철, 박은종，이준환， "벡터 마디언을 이용한 비디오 영상의 온랴인 배정 추출" 정보처리학회 논문지， 저113권 B권， 제5호， pp,226-280, 2000.
  22. F .H. Cbeng and Y. L. Chen, "Real Time Multiple Objects Tracking and Identification based on Discrete Wavelet Transforrn," Pattern Hecognition, Vol.39, pp. 1126-1139, 2003.