The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Detection of Objects Temporally Stop Moving with Spatio-Temporal Segmentation

시공간 영상분할을 이용한 이동 및 이동 중 정지물체 검출

  • Received : 2014.11.27
  • Accepted : 2014.12.22
  • Published : 2015.01.30
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Abstract

This paper proposes a method for detection of objects temporally stop moving in video sequences taken by a moving camera. Even though the consequence of missed detection of those objects could be catastrophic in terms of application level requirements, not much attention has been paid in conventional approaches. In the proposed method, we introduce cues for consistent detection and tracking of objects: motion potential, position potential, and color distribution similarity. Integration of the three cues in the graph-cut algorithm makes possible to detect objects that temporally stop moving and are newly appearing. Experiment results prove that the proposed method can not only detect moving objects but also track objects stop moving.

본 논문에서는 이동 카메라 환경에서 이동 및 이동 중 정지물체를 검출하기 위한 방법을 제안한다. 이동 중에 일시적으로 정지한 물체는 검출 결과의 응용관점에서 볼 때 이동물체의 검출만큼이나 중요한데, 기존의 이동물체 검출 방법들은 이들을 배경과 구분하지 못하는 한계를 갖는다. 이러한 문제를 해결하기 위해 제안하는 방법에서는 이동 가능성 큐, 위치 가능성 큐, 그리고 색 분포 유사성 큐를 정의하여 이동물체 검출 및 지속적인 추적에 이용한다. 그래프 컷 알고리즘은 세 개의 큐를 결합하여 시공간 영상분할을 수행함으로써 이동 및 이동 중 정지물체를 검출한다. 제안하는 방법은 이동물체 뿐 아니라 이동 중 정지물체에 대해서도 검출이 가능함을 실험을 통해 증명하였다.

Keywords

References

  1. J. M. Choi, H. Song, S. H. Park, and C. D. Lee, "A Study on the Object Extraction and Tracking System for Intelligent Surveillance," J. Commun. Networks (JCN), vol. 38B, no. 7, pp. 589-595, Jul. 2013. https://doi.org/10.7840/kics.2013.38B.7.589
  2. J. Kwon and D.-S. Kim, "Detecting moving objects from slowly moving viewpoint for automotive rear view camera systems," in Proc. IEEE Int. Conf. Consumer Electron., pp. 51-52, 2014.
  3. A. Wedel, A. Meissner, C. Rabe, U. Franke, and D. Cremers, "Detection and segmentation of independently moving objects from dense scene flow," in Proc. Int. Conf. Energy minimization methods in computer vision and pattern recognition, pp. 14-27, 2009.
  4. C. Yuan, G. Medioni, J. Kang, and I. Cohen, "Detecting motion regions in the presence of a strong parallax from a moving camera by multiview geometric constraints," IEEE Trans. Pattern Anal. Machine Intell., vol. 29, no. 9, pp. 1627-1641, 2007. https://doi.org/10.1109/TPAMI.2007.1084
  5. E. Einhorn, M. Filzhuth, C. Schroter, and H.-M. Gross, "Monocular detection and estimation of moving obstacles for robot navigation," in Eur. Conf. Mob. Robots, pp. 121-126, 2011.
  6. R. E. Kalman, "A new approach to linear filtering and prediction problems," J. Basic Eng., pp. 35-45, 1960.
  7. Z. Kalal, K. Mikolajczyk, and J. Matas, "Tracking-learning-detection," IEEE Trans. Pattern Anal. Machine Intell., vol. 34, pp. 1409-1422, 2012. https://doi.org/10.1109/TPAMI.2011.239
  8. Y. Wang, J. Ostermann, and Y. Q. Zhang, Video processing and communications, Upper Saddle River, Prentice Hall, 2002.
  9. S. Leutenegger, M. Chli, and R. Y. Siegwart, "BRISK: Binary robust invariant scalable keypoints," in Proc. IEEE Int. Conf. Comput. Vis., pp. 2548-2555, 2011.
  10. T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, "High accuracy optical flow estimation based on a theory for warping," in Proc. European Conf. Comput. Vis., pp. 25-36, 2004.
  11. R. Hartley and A. Zisserman, Multiple view geometry in computer vision, Cambridge university press, 2003.
  12. P. H. S. Torr and D. W. Murray, "The development and comparison of robust methods for estimating the fundamental matrix," Int. J. Comput. Vis., vol. 24, pp. 271-300, 1997. https://doi.org/10.1023/A:1007927408552
  13. R. I. Hartley, "In defense of the eight-point algorithm," IEEE Trans. Pattern Anal. Machine Intell., vol. 19, pp. 580-593, 1997. https://doi.org/10.1109/34.601246
  14. C. Harris and M. Stephens, "A combined corner and edge detector," in Proc. The Fourth Alvey Vis. Conf., pp. 147-151, 1988.
  15. C. Rother, V. Kolmogorov, and A. Blake, "Grabcut: Interactive foreground extraction using iterated graph cuts," ACM Trans. Graphics, pp. 309-314, 2004.
  16. C. M. Bishop, Pattern recognition and machine learning, springer New York, 2006.
  17. A. Delong, A. Osokin, H. N. Isack, and Y. Boykov, "Fast approximate energy minimization with label costs," Int. J. Comput. Vis., vol. 96, pp. 1-27, 2012. https://doi.org/10.1007/s11263-011-0437-z
  18. J. Weng, P. Cohen, and M. Herniou, "Camera calibration with distortion models and accuracy evaluation," IEEE Trans. Pattern Anal. Machine Intell., vol. 14, pp. 965-980, 1992. https://doi.org/10.1109/34.159901

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