DOI QR코드

DOI QR Code

The Interesting Moving Objects Tracking Algorithm using Color Informations on Multi-Video Camera

다중 비디오카메라에서 색 정보를 이용한 특정 이동물체 추적 알고리듬

  • 신창훈 (청주대학교 대학원 전자공학과) ;
  • 이주신 (청주대학교 전자공학과)
  • Published : 2004.06.01

Abstract

In this paper, the interesting moving objects tracking algorithm using color information on Multi-Video camera is proposed Moving objects are detected by using difference image method and integral projection method to background image and objects image only with hue area, after converting RGB color coordination of image which is input from multi-video camera into HSI color coordination. Hue information of the detected moving area are normalized by 24 steps from 0$^{\circ}$ to 360$^{\circ}$ It is used for the feature parameters of the moving objects that three normalization levels with the highest distribution and distance among three normalization levels after obtaining a hue distribution chart of the normalized moving objects. Moving objects identity among four cameras is distinguished with distribution of three normalization levels and distance among three normalization levels, and then the moving objects are tracked and surveilled. To examine propriety of the proposed method, four cameras are set up indoor difference places, humans are targeted for moving objects. As surveillance results of the interesting human, hue distribution chart variation of the detected Interesting human at each camera in under 10%, and it is confirmed that the interesting human is tracked and surveilled by using feature parameters at four cameras, automatically.

본 논문은 다중 비디오카메라에서 색 정보를 이용한 특정 이동물체 추적 이동물체 추적 알고리듬을 제안한다. 제안된 방법은 다중 비디오카메라로부터 입력되는 영상의 RGB 칼라 좌표계를 HSI 칼라 좌표계로 변환한 후, 영상의 색조 영역만을 가지고 배경영상과 물체가 존재하는 영상에서 차영상 기법과 가산투영 기법을 사용하여 이동물체를 검출한다. 검출된 이동물체 영역의 색조는 0도부터 360도 사이에서 24단계로 정규화 된다. 정규화된 이동물체의 색조 분포도를 구한 후, 가장 높은 분포를 갖는 3개의 정규화 레벨과 3개의 정규화 레벨 사이의 간격을 이동물체의 특징파라미터로 사용하였다. 각 카메라간의 이동물체 동일성 관별은 이동물체 특징파라미터를 가지고 판별하고, 추적 감시하였다. 제안된 방법의 타당성을 검토하기 위하여 실내에 각기 다른 장소에 4대의 카메라를 각각 설치하여 이동물체의 대상을 사람으로 놓고, 특정사람을 감시한 결과 각 카메라에서 검출된 특정사람의 색조분포도 변화는 10%내를 유지함을 보였고, 특징 파라미터로 4대의 카메라에서 특정사람이 자동 추적감시 됨을 확인하였다.

Keywords

References

  1. Tsai-Hong Hong, Tommy Chang, Chritopher Rasmusen and Michael shneier, 'Feature Detection and Tracking for Mobile Robots Using a Combination of Ladar and Color images,' Proceedings of the 2002 IEEE International Conference onRobotics & Automation Washington DC, pp. 4330-4345, May, 2002 https://doi.org/10.1109/ROBOT.2002.1014443
  2. 서동하, 임재혁, 원치선, 'HSV 칼라를 이용한 블록단위 영상 분할', 2000년 제13회 신호처리학술대회 논문집, 제13권 제1호, pp.651-654
  3. Ng Kim Piau and surendra Ranganath 'Tracking People,' Pattern Recognition, 2002. Proceedings. 16th International Conference on Publication Date, Vol.2, pp.370-373, 2002
  4. George V. Paul, Glenn J. Beach and Charles J. Cohen, 'A Realtime Object Tracking System using a Color Camera,' 30th Applied Imagery Pattern Recognition Workshop (AIPR '01) Washington, D. C. pp.137-142, October, 200l https://doi.org/10.1109/AIPR.2001.991216
  5. Greg T. Kogut and Mohan M. Trivedi, 'Real-time Wide Area Tracking: Hardware and Software Infrastructure,' The IEEE 5th International conference on Intelligent Transportation Systems, Singapore, September, 2002 https://doi.org/10.1109/ITSC.2002.1041284
  6. D. Beymer and K. Konolige, 'Real-time Tracking of Multiple People using Stereo,' In IEEE Frame Rate Workshop, 1999
  7. A Bobick and J. Davis 'Real-time recognition of Activity using Temporal Templates,' In IEEE Workshop on application of Computer Vision, pp.1233-1251, 1996 https://doi.org/10.1109/ACV.1996.571995
  8. Ismail Haritaoglu and Myron Flickner, 'Detection and Tracking of Shopping Groups in Stores,' in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Kauai, Hawaii, pp. -431-I-438, 2001 https://doi.org/10.1109/CVPR.2001.990507
  9. Zoran Durie Fayin Li, Yan sun and Harry Wechsler, 'Using Normal flow for Detection and Tracking of Limbs in Color images'
  10. Gi-jeong lang and In-So Kweon 'Robust Object Tracking Using an Adaptive Color Model,' Proceedings of the 2001 IEEE International conference on Robotics & Automation Seoul, Korea, pp.1677-1682, May, 2001 https://doi.org/10.1109/ROBOT.2001.932852
  11. J. Yang and A. Waibel, 'A Real-Time Face Tracker,' Proceeding of WACV, pp.142-147, 1996 https://doi.org/10.1109/ACV.1996.572043
  12. M. J. Jones and J. M. Rehg, 'Statistical Color Models with Aplocation to Skin Detection,' Proc. CVPR, pp.274-280, 1999
  13. J. Krumm, et al., 'Multi-camera Multi-person Tracking for EasyLing,' Third IEEE International Workshop on Visual Surveillance 2000, 2000 https://doi.org/10.1109/VS.2000.856852
  14. S. J. McKenna, et al., 'Tracking Interacting People,' Proceeding of Fourth IEEE Conference on Automatic Face and Gesture Recognition, pp.348-353, 2000 https://doi.org/10.1109/AFGR.2000.840658
  15. Rafael C. Gonzalez and Richard E. Woods, 'Digital Image Processing,' Addison Wesley Longman, 1992
  16. Quan Chun, 'A Study on Real-time Tracking of Moving Object Based on Fast Matching Algorithm,' Ph. D. Paper, 2003
  17. Chris Stauffer and W. Eric L. Grimson, 'Learning Patterns of Activity Using Real-Time Tracking,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No.8, August, 2000 https://doi.org/10.1109/34.868677
  18. Collins, Lipton, Kanande, Fugiyoshi, duggins, Tsin, Tolliver, Enomoto and Hasegawa, 'A System for Video surveillance and Monitoring', VSAM Fianl Report Technical Report CMURI-TR-00-12, Robotics Institute, CMU, May, 2000
  19. 김준식, 박래홍, 이병욱, '가산투영을 이용한 2단계 고속 블록 정합 알고리듬', 전자공학회논문지B, 제30-B편 제1호, pp.45-54, 1993