DOI QR코드

DOI QR Code

Video object segmentation using a novel object boundary linking

새로운 객체 외곽선 연결 방법을 사용한 비디오 객체 분할

  • 이호석 (호서대학교 공과대학 컴퓨터공학 뉴미디어학과)
  • Published : 2006.06.01

Abstract

Moving object boundary is very important for the accurate segmentation of moving object. We extract the moving object boundary from the moving object edge. But the object boundary shows broken boundaries so we develop a novel boundary linking algorithm to link the broken boundaries. The boundary linking algorithm forms a quadrant around the terminating pixel in the broken boundaries and searches for other terminating pixels to link in concentric circles clockwise within a search radius in the forward direction. The boundary linking algorithm guarantees the shortest distance linking. We register the background from the image sequence using the stationary background filtering. We construct two object masks, one object mask from the boundary linking and the other object mask from the initial moving object, and use these two complementary object masks to segment the moving objects. The main contribution of the proposed algorithms is the development of the novel object boundary linking algorithm for the accurate segmentation. We achieve the accurate segmentation of moving object, the segmentation of multiple moving objects, the segmentation of the object which has a hole within the object, the segmentation of thin objects, and the segmentation of moving objects in the complex background using the novel object boundary linking and the background automatically. We experiment the algorithms using standard MPEG-4 test video sequences and real video sequences of indoor and outdoor environments. The proposed algorithms are efficient and can process 70.20 QCIF frames per second and 19.7 CIF frames per second on the average on a Pentium-IV 3.4GHz personal computer for real-time object-based processing.

비디오에서 움직이는 객체의 외곽선은 객체를 정확하게 분할하기 위하여 매우 중요하다. 그러나 움직이는 객체의 외곽선에는 단락된 외곽선들이 존재하게 된다. 우리는 단락된 외곽선을 연결할 수 있는 새로운 외곽선 연결 알고리즘을 개발하였다. 외곽선 연결 알고리즘은 단락된 외곽선의 말단 픽셀에 사분면을 형성하고 동심원을 구성하면서 반지름 내에서 다른 말단 픽셀을 찾는 탐색을 전진하면서 수행한다. 외곽선 연결 알고리즘은 객체의 외곽선에서 가장 짧게 외곽선을 연결한다. 그리고 시스템은 비디오로부터 배경을 구하여 저장한다. 시스템은 외곽선 연결로부터 객체 마스크를 생성하고, 배경된 저장으로부터 또 하나의 객체 마스크를 생성하여 이 두 개의 객체 마스크를 보완적으로 사용하여 움직이는 객체를 분할한다. 논문의 주요 장점은 정확한 객체 분할을 위한 새로운 객체 외곽선 연결 알고리즘의 개발이다. 제안된 알고리즘은 개발된 새로운 객체 외곽선 연결 알고리즘과 배경 저장을 이용하여 정확한 객체 분할, 다중 객체 분할, 내부에 구멍이 존재하는 객체의 분할, 가느다란 객체의 분할, 그리고 복잡한 배경을 가진 객체를 자동으로 분할하여 보여주었다. 우리는 알고리즘들을 표준 MPEG-4 실험 영상과 카메라로 입력된 실제 영상을 가지고 실험하였다. 제안된 알고리즘들은 매우 효율이 좋으며 펜티엄-IV 3.4GHz CPU에서 평균적으로 QCIF 영상을 1초당 70.20 프레임 그리고 CIF 영상을 1초당 19.7 프레임을 실시간 객체 응용을 위하여 처리할 수 있다.

Keywords

References

  1. Changick Kim, Jenq-Neng Hwang, 'Fast and Automatic Video Object Segmentation and Tracking for Content-Based Applications,' IEEE Trans. on Circuits and Systems for Video Technology, Vol.12, No.2, pp.122-129, Feb., 2002 https://doi.org/10.1109/76.988659
  2. Changick Kim, Jenq-Neng Hwang, 'Object-Based Video Abstraction for Video Surveillance Systems,' IEEE Trans. on Circuits and Systems for Video Technology, Vol.12, No.12, pp.1128-1138, Dec., 2002 https://doi.org/10.1109/TCSVT.2002.806813
  3. Thomas Meier, King N. Ngan, 'Video Segmentation for Content-Based Coding,' IEEE Trans. on Circuits and Systems for Video Technology, Vol.9, No.8, pp.1190-1203, Dec., 1999 https://doi.org/10.1109/76.809155
  4. Shao-Yi Chien, Shyh-Yih Ma, Liang-Gee Chen, 'Efficient Moving Object Segmentation Algorithm Using Background Registration Technique,' IEEE Trans. on Circuits and Systems for Video Technology, Vol.12, No.7, pp.577-586, July, 2002 https://doi.org/10.1109/TCSVT.2002.800516
  5. Shao-Yi Chien, Yu-Wen Huang, Liang-Gee Chen, 'Predictive Watershed : A Fast Watershed Algorithm for Video Segmentation,' IEEE Trans. on Circuits and Systems for Video Technology, Vol.13, No.5, pp.453-461, May, 2003 https://doi.org/10.1109/TCSVT.2003.811605
  6. Fu Chang, Chun-Jen Chen, Chi-Jen Lu, 'A linear-time component-labeling algorithm using contour tracing technique,' Computer Vision and Image Understanding 93, pp.206-220, 2004 https://doi.org/10.1016/j.cviu.2003.09.002
  7. Luis Ducla Soares, Fernando Pereira, 'Spatial Shape Error Concealment for Object-Based Image and Video Coding,' IEEE Trans. on Image Processing, Vo.13, No.4, pp.586-599, April, 2004 https://doi.org/10.1109/TIP.2004.823826
  8. Shahram Shirani, Berna Erol, Faouzi Kossentini, 'A Concealment Method for Shape Information in MPEG-4 Coded Video Sequences,' IEEE Trans. on Multimedia, Vol.2, No.3, pp.185-190, Sep., 2000 https://doi.org/10.1109/6046.865483
  9. Hans Georg Mussmann, Michael Hotter, Jorn Ostermann, 'Object-oriented analysis-synthesis coding of moving images,' Signal Processing : Image Communications, Vol.1, No.2, pp.117-138, Oct., 1989 https://doi.org/10.1016/0923-5965(89)90005-2
  10. Peter Gerken, Michael Wollborn, Stefan Schultz, 'Polygon/spline approximation of arbitrary region shapes as proposal for MPEG-4 tool evaluation-Technical description,' ISO/IEC JTC1/SC29/WG11 MPEG 95/360, Nov., 1995
  11. Fabian W. Meier, Guido M. Schuster, Aggelos K. Katsaggelos, 'A Mathematical Model for Shape Coding with B-Splines,' Signal Processing : Image Communication, Vol.1, pp.685-701, 2000 https://doi.org/10.1016/S0923-5965(99)00045-4
  12. Daniel P. Huttenlocher, Gregory A. Klanderman, William J. Rucklidge, 'Comparing Images Using the Hausdorff Distance,' IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.15, No.9, pp.850-863, Sept., 1993 https://doi.org/10.1109/34.232073
  13. John Canny, 'A computational approach to edge detection,' IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.8, No.6, pp.679-698, Nov., 1986 https://doi.org/10.1109/TPAMI.1986.4767851
  14. Roland Mech, Michael Wollborn, 'A noise robust method for 2D shape estimation of moving objects in video sequence considering a moving camera,' Signal Processing, Vol.66, pp.203-217, Sept., 1997 https://doi.org/10.1016/S0165-1684(98)00006-1
  15. Til Aach, Andre Kaup, Rudolf Mester, 'Statistical model-based change detection in moving video,' Signal Processing, Vol.31, pp.165-180, March, 1993 https://doi.org/10.1016/0165-1684(93)90063-G
  16. Emrullah Durucan, Touradj Ebrahimi, 'Moving Object Detection Between Multiple and Color Images,' IEEE Conf. on Advanced Video and Signal Based Surveillance (AVSS'03), pp.243-251, July 21-23, Miami Florida, 2003 https://doi.org/10.1109/AVSS.2003.1217928
  17. Emrullah Durucan, Touradj Ebrahimi, 'Change Detection and Background Extraction by Linear Algebra,' Proc. of the IEEE, Vol.89, No.10, pp.1368-1381, October, 2001 https://doi.org/10.1109/5.959336
  18. Linda Shapiro, George Stockman, Computer Vision, Prentice-Hall, Inc., 2001
  19. Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Prentice-Hall, Inc., 2002
  20. Amjad Hajjar, Tom Chen, 'A VLSI Architecture for Real-Time Edge Linking,' IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.21, No.1, pp.89-94, Jan., 1999 https://doi.org/10.1109/34.745740
  21. Jurgen Stauder, Roland Mech, Jorn Ostermann, 'Detection of Moving Cast Shadows,' IEEE Trans. on Multimedia, Vol.1, No.1, pp.65-76, March, 1999 https://doi.org/10.1109/6046.748172