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Multiple Moving Object Detection Using Different Algorithms

이종 알고리즘을 융합한 다중 이동객체 검출

  • Heo, Seong-Nam (School of Electronics Engineering, Kyungpook National University) ;
  • Son, Hyeon-Sik (School of Electronics Engineering, Kyungpook National University) ;
  • Moon, Byungin (School of Electronics Engineering, Kyungpook National University)
  • Received : 2015.06.08
  • Accepted : 2015.08.18
  • Published : 2015.09.30

Abstract

Object tracking algorithms can reduce computational cost by avoiding computation over the whole image through the selection of region of interests based on object detection. So, accurate object detection is an important task for object tracking. The background subtraction algorithm has been widely used in moving object detection using a stationary camera. However, it has the problem of object detection error due to incorrect background modeling, whereas the method of background modeling has been improved by many researches. This paper proposes a new moving object detection algorithm to overcome the drawback of the conventional background subtraction algorithm by combining the background subtraction algorithm with the motion history image algorithm that is usually used in gesture detection. Although the proposed algorithm demands more processing time because of time taken for combining two algorithms, it meet the real-time processing requirement. Moreover, experimental results show that it has higher accuracy compared with the previous two algorithms.

객체 추적 알고리즘들은 객체 인식 결과를 이용한 관심영역 설정을 통해 영상 전체에 대한 연산이 수행되는 것을 방지하여 연산량을 줄일 수 있다. 따라서 객체 인식 알고리즘의 정확한 객체 검출은 객체 추적에서 매우 중요한 과정이다. 고정된 카메라를 기반으로 하여 이동하는 객체를 검출 하는 방법으로 배경 차 알고리즘이 널리 사용되어왔고 많은 연구에 의해 배경 모델링 방법이 개선되면서 배경 차 알고리즘의 성능이 개선되었으나 여전히 정확하지 못한 배경 모델링에 의한 객체 오검출의 문제를 가진다. 이에 본 논문에서는 제스쳐 인식에 주로 사용되는 모션 히스토리 이미지 알고리즘을 배경 차 알고리즘과 융합하여 기존의 배경 차 알고리즘이 가지는 문제점을 극복할 수 있는 다중 이동객체 검출 알고리즘을 제안한다. 제안하는 알고리즘은 융합 과정 추가로 수행시간이 다소 길어지나 실시간성을 만족하며 기존의 배경 차 알고리즘에 비해 높은 정확도를 가짐을 실험을 통해 확인하였다.

Keywords

References

  1. T. W. Jang and J. B. Kim, "Automatic CCTV control system based on ubiquitous computing," J. KICS, vol. 37, no. 3, pp. 96- 102, Jun. 2012.
  2. B. W. Chung, K. Y. Park, and S. Y. Hwang, "A fast and efficient Haar-like feature selection algorithm for object detection," J. KICS, vol. 38, no. 6, pp. 486-491, Jun. 2013.
  3. T. W. Jang, Y. T. Shin, and J. B. Kim, "A study on the object extraction and tracking system for intelligent surveillance," J. KICS, vol. 38, no. 7, pp. 589-595, Jul. 2013.
  4. C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, "Pfinder: real-time tracking of the human body," IEEE Trans. Pattern Anal. Machine Intell., vol. 19, no. 7, pp. 780-785, Jul. 1997. https://doi.org/10.1109/34.598236
  5. D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russell, "Towards robust automatic traffic scene analysis in real-time," in Proc. 12th IAPR Int. Conf. Pattern Recognition, vol. 1, pp. 126-131, Jerusalem, Israel, Oct. 1994.
  6. B. P. L. Lo and S. A. Velastin, "Automatic congestion detection system for underground platforms," in Proc. 2001 Int. Symp. Intell. Multimedia, Video Speech Process., pp. 158- 161, Hong Kong, May 2001.
  7. R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, "Detecting moving objects, ghosts, and shadows in video streams," IEEE Trans. Pattern Anal. Machine Intell., vol. 25, no. 10, pp. 1337-1342, Oct. 2003. https://doi.org/10.1109/TPAMI.2003.1233909
  8. C. Stauffer and W. E. L. Grimson, "Adaptive background mixture models for real-time tracking," in Proc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 246-252, Fort Collins, Colorado, Jun. 1999.
  9. P. Massimo, "Background subtraction techniques: A review," in Proc. IEEE Int. Conf. Systems, Man and Cybernetics, vol. 4, pp. 3099-3104, The Hague, Netherlands, Oct. 2004.
  10. P. W. Power and J. A. Schoonees, "Understanding background mixture models for foreground segmentation," in Proc. 17th Int. Conf. Image Vision Comput. New Zealand, pp. 267-271, Auckland, New Zealand, Nov. 2002.
  11. A. F. Bobick and J. W. Davis, "The recognition of human movement using temporal templates," IEEE Trans. Pattern Anal. Machine Intell., vol. 23, no. 3, pp. 257- 267, Mar. 2011.
  12. G. R. Bradski and J. W. Davis, "Motion segmentation and pose recognition with motion history gradients," Machine Vision Appl., vol. 13, no. 3, pp. 174-184, Jul. 2002. https://doi.org/10.1007/s001380100064

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