Region-Based Moving Object Segmentation for Video Monitoring System

비디오 감시시스템을 위한 영역 기반의 움직이는 물체 분할

  • 이경미 (구미1대학교 컴퓨터정보계열) ;
  • 김종배 (경북대학교 컴퓨터공학과) ;
  • 이창우 (경북대학교 컴퓨터공학과) ;
  • 김항준 (경북대학교 컴퓨터공학과)
  • Published : 2003.01.01

Abstract

This paper presents an efficient region-based motion segmentation method for segmenting of moving objects in a traffic scene with a focus on a Video Monitoring System (VMS). The presented method consists of two phases: motion detection and motion segmentation. Using the adaptive thresholding technique, the differences between two consecutive frames are analyzed to detect the movements of objects in a scene. To segment the detected regions into meaningful objects which have the similar intensity and motion information, the regions are initially segmented using a k-means clustering algorithm and then, the neighboring regions with the similar motion information are merged. Since we deal with not the whole image, but the detected regions in the segmentation phase, the computational cost is reduced dramatically. Experimental results demonstrate robustness in the occlusions among multiple moving objects and the change in environmental conditions as well.

본 논문은 비디오 영상에서 움직이는 물체를 분할하는 방법을 제안한다. 물체들의 크기가 작거나 서로 겹쳐있을 경우(occlusion), 또는 잡음이 많은 경우에도 안정적인 이 방법은 움직임 검출(motion detection)과 움직임 분할(motion segmentation) 두 단계로 구성되어 있다. 움직임 검출을 하기 위하여 인접 영상간의 차영상(difference image) 분석을 통해 움직임이 있는 부분을 추출하며, 이때 적응적 임계치 방법을 이용하여 빛의 변화나 노이즈가 포함된 환경에서도 안정적으로 추출한다. 움직임 분할 단계에서는 움직임이 검출된 부분을 초기영역으로 분할 한 뒤, 이 영역들의 모션정보에 따라 이웃 한 영역들을 병합함으로써 독립적으로 움직이는 물체를 분할한다. 이러한 방법은 검출된 영역에 대해서만 움직임 분할을 함으로 많은 계산효과를 얻을 수 있으며 실제 도로영상에서 제안된 방법을 실험해본 결과 비디오 감시시스템에 적합함을 알 수 있었다.

Keywords

References

  1. I. Haritaoglu, D. Harwood, L.S. Davis, 'Real-Time Surveillance of People and Their Activities', IEEE Trans. PAMI, Vol. 22, No. 8, pp. 809-830, 2000 https://doi.org/10.1109/34.868683
  2. J. B. Kim, C. W. Lee, K M Lee, T. S. Yun, H. J. Kim, 'Wavelet-Based Vehicle Tracking for Automatic Surveillance System', in Proc. IEEE TENCON, Vol. 1, pp. 313-316, 2001 https://doi.org/10.1109/TENCON.2001.949604
  3. G. L. Foresti, 'A Real-Time System for Video Surveillance of Unattended Outdoor Environments'. IEEE Trans. PAMI, Vol. 8, No.6, pp. 697-704, 1998 https://doi.org/10.1109/76.728411
  4. F. Moscheni, S. Bhattacharjee, M. Kunt, 'Spatiotemporal segmentation based on region merginging', IEEE Trans. PAMI, Vol. 20, No. 9, pp. 897-915, 1998 https://doi.org/10.1109/34.713358
  5. AL. Bovik, Handbook of Image and Video Processing, Academic Press, San Diego, USA, 2000
  6. J. Badenas, M. Bober, and F. Pla, 'Segmenting Traffic Scenes from Grey Level and Motion Information', Pattern Analysis & Applications, Vol. 4, pp. 28-38, 2001 https://doi.org/10.1007/s100440170022
  7. J. Y. A. Wang and E. H. Adelson, 'Representing moving images with layers,' IEEE Trans. Image Processing, vol. 3, No. 5,pp. 625-638,Sept. 1994 https://doi.org/10.1109/83.334981
  8. M. Irani, S. Peleg, 'Motion analysis for image enhance-rent: Resolution, occlusion, and transparency', Int. Journal of Visual Comm. Image Rep., Vol. 4, No.4, pp. 324-335, 1993 https://doi.org/10.1006/jvci.1993.1030
  9. M.M.Chang, A.M. Tekalp, and M.I. Sezan, 'An algorithm for simultaneous motion estimation and scene segmentation,' Proc. IEEE Int. Conf. Acoust. Speech Sign. Proc., Adelaide, Australia, April 1994 https://doi.org/10.1109/ICASSP.1994.389492
  10. E. Y. Kim, S. H. Park, H. J. Kim, 'A Genetic Algorithm based Segmentation of Markov Random Field Modeled Images', IEEE Signal Processing Letters, Vol. 7, No. 11, pp. 301-303, 2000 https://doi.org/10.1109/97.873564
  11. E. Y. Kim, S.W. Hwang, S.H. Park, and H J. Kim 'Spatiotemporal Segmentation Using Genetic Algorithms', Pattern Recognition, Vol. 34, No. 10, pp. 2063-2066, 2001 https://doi.org/10.1016/S0031-3203(00)00129-1
  12. R. C. Gonzalez, R. E. Woods, Digital Image Processing, Prentice Hall, Upper Saddle River, New Jersey, USA, 2002
  13. H. Nariman, M. Alireza, B. Neil, 'Automatic Thresholding for Change Detection in Digital Video', in Proc. SPIE 4067, pp. 133-142, 2000 https://doi.org/10.1117/12.386578
  14. J. Liu and Y. H. Yang, 'Multiresolution color Image Segmentation', IEEE Trans. PAMI, Vol. 16, No. 7, pp. 689-700, 1994 https://doi.org/10.1109/34.297949
  15. Electronics and Communications in Japan, Part 3, Vol. 82, No. 11, pp. 527-535, 1999