한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제16권6호
  • /
  • Pages.89-97
  • /
  • 2011
  • /
  • 1598-849X(pISSN)
  • /
  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
권호 표지
DOI QR코드

DOI QR Code

다중 카메라를 이용한 객체추적과 증강현실의 구현

Implementation of augmented reality and object tracking using multiple camera

  • 김학희 (한국기술교육대학교 컴퓨터공학과)
  • Kim, Hag-Hee (Dept. of Computer Engineering, Korea University of Technology and Education)
  • 투고 : 2011.02.09
  • 심사 : 2011.02.28
  • 발행 : 2011.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

현재의 객체 추적과 검색의 과정을 보면 고정된 단일 카메라를 통해 입력받은 영상에서 객체를 추출하여 추적하고, 추적된 객체의 구체적인 정보를 알기 위해 줌(Zoom) 기능으로 객체를 인식하는 과정이었다. 본 논문은 다중카메라를 이용하여 객체를 추적하고, 인식하는 것으로써 추적된 객체에 대해 검색인식가능한 영역에 대한 정보를 증강현실로 나타내는 시스템을 제안한다. 제안 시스템의 실험결과를 보면 연산에 포함된 픽셀 수가 현저히 줄어들고, 객체의 인식률이 향상되고, 정보 확인 시간도 단축되었다. 그리고 기존 방법과 비교하여 객체의 움직임을 검출하는 정확성은 물론 움직임 검출에 소요되는 시간도 단축되어 개선된 성능을 보였다.

When examining current process of object tracking and search, objects were tracked by extracting them from image that was inputted through fixed single camera and objects were recognized through Zoom function to know detailed information on objects tracked. This study proposed system that expresses information on area that can seek and recognize object tracked as augmented reality by recognizing and seeking object by using multi camera. The result of experiment on proposed system showed that the number of pixels that was included in calculation was remarkably reduced and recognition rate of object was enhanced and time that took to identify information was shortened. Compared with existing methods, this system has advantage of better accuracy that can detect the motion of object and advantage of shortening time that took to detect motion.

키워드

참고문헌

  1. NVIDIA CUDA Compute unified device architecture November - programming guide. ${\copyright}$ Copyright, NVIDIA Corporation (2007)
  2. Rita Cucchiara, Andrea Prati, and Roberto Vezzani: Advanced video surveillance with pan tilt zoom cameras. Proc. of Workshop on Visual Surveillance (VS) at ECCV 2006, May (2006)
  3. Peter I. Corke and Malcolm C. Good: Dynamic effects in visual closed-loop systems. IEEE Transactions on Robotics and Automation, vol. 12, no. 5, October (1996), pp. 671-683 https://doi.org/10.1109/70.538973
  4. L. Jordao, M. Perrone, J. P. Costeira, J. Santos-Victor: Active face and feature tracking. Image Analysis and Processing 1999, (1999) pp. 572-576
  5. I. D. Reid and D. W. Murray : Active tracking of foveated feature clusters using affine structure. International Journal of Computer Vision, vol. 18, no. 1, 1996
  6. L. Gu and J. Su : Gaze control on humanoid robot head. IEEE World Congress on Intelligent Control and Automation, pp.9144-9148
  7. C. Brown and D. Coombs : Real-time binocular smooth pursuit tracking. International Journal of Computer Vision. Vol. 11, no.2, (1993), pp.147-164 https://doi.org/10.1007/BF01469226
  8. Y. C. Chen, L. W. Chang, and C. T. Hsu: Feature-based object tracking with an active camera, PCM2002, LNCS2532, (2002) pp.1137-1144
  9. Mutsumi Watanabe, Nobuyuki Takeda, and Kazunori Onoguchi: A moving object recognition method by optical flow analysis. Proceedings of ICPR, 1051-4651/96, (1996) pp.528-533
  10. Don Murray and Anup Basu: Motion tracking with an active camera. IEEE transactions on pattern analysis and machine intelligence, vol. 16, no. 5, (1994) pp.449-459 https://doi.org/10.1109/34.291452
  11. Shoichi Araki, Takashi Matsuoka, Naokazu Yokoya and Haruo Takemura: Real time tracking of multiple moving object contrours in a moving camera image sequence. IEICE TRANS. INF. & SYST., vol. E83-D, No. 7, (2000) pp. 1583-1591
  12. Arindam Biswas, Prithwijit Guha, Amitabha Mukerjee, K.S. Venkatesh: Intrusion detection and tracking with pan-tilt cameras. IET International Conference, (2006) pp. 565-571
  13. Tamer Inanc, Mario Sznaier and Octavia Camps: Robust identification and model (in)validation of active-vision systems. Identification and control, ${\copyright}$ Copyright, Springer London, (2007), pp.167-200
  14. Petros Maragos: Morphological filtering for image enhancement and feature detection. The image and Video Processing Handbook, 2nd edition, (2004) pp.135-156