대한기계학회논문집A (Transactions of the Korean Society of Mechanical Engineers A)

  • 제37권12호
  • /
  • Pages.1445-1452
  • /
  • 2013
  • /
  • 1226-4873(pISSN)
  • /
  • 2288-5226(eISSN)
대한기계학회 (The Korean Society of Mechanical Engineers)
권호 표지
DOI QR코드

DOI QR Code

영상정보만을 이용한 사람과 로봇간 실시간 상대위치 추정 알고리즘

Real-Time Algorithm for Relative Position Estimation Between Person and Robot Using a Monocular Camera

  • 이정욱 (삼성전자) ;
  • 선주영 (충남대학교 메카트로닉스공학과) ;
  • 원문철 (충남대학교 메카트로닉스공학과)
  • Received : 2012.09.24
  • Accepted : 2013.11.07
  • Published : 2013.12.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

Abstract

본 논문에서는 단안 카메라를 이용하여 사람과 로봇(카메라)간의 상대위치를 실시간으로 추정하는 알고리즘을 제안한다. HOG(기울기 히스토그램) 특징벡터와 SVM(서포트 벡터 머신) 분류기를 이용하여 사람의 두부 및 어깨영역을 검출한다. 검출된 영역의 크기와 위치를 이용하여 사람과 로봇(카메라)간의 상대 위치 및 각도를 계산한다. 또한 알고리즘 수행속도를 향상시키기 위하여 본 논문에서는 NVIDIA의 GPU와 CUDA 라이브러리를 사용하였다. 그 결과 알고리즘 수행속도는 초당 15 프레임의 영상데이터를 처리할 수 있다. 알고리즘의 정확도 비교를 위해서 SICK 레이저 스캐너 출력과 비교하였다.

In this paper, we propose a real-time algorithm for estimating the relative position of a person with respect to a robot (camera) using a monocular camera. The algorithm detects the head and shoulder regions of a person using HOG (Histogram of Oriented Gradient) feature vectors and an SVM (Support Vector Machine) classifier. The size and location of the detected area are used for calculating the relative distance and angle between the person and the camera on a robot. To increase the speed of the algorithm, we use a GPU and NVIDIA's CUDA library; the resulting algorithm speed is ~ 15 Hz. The accuracy of the algorithm is compared with the output of a SICK laser scanner.

Keywords

References

  1. Enzweiler, M. and Gavrila, D., 2009, "Monocular Pedestrian Detection : Survey and Experiments," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 13, pp. 2179-2195.
  2. Dalal, N. and Triggs, B., 2005, "Histogram of Oriented Gradients for Human Detection," IEEE Conference on Computer Vision and Pattern Recognition(CVPR).
  3. Broggi, A., Bertozzi, M. and Fascioli, A., 2000, "Shape Based Pedestrian Detection," In Proc. of IEEE Intelligence Vehicles Symposium 2000, pp.215-220.
  4. Fang, Y., Yamada, K., Nunomiya, Y., B.H.P., H. and Masaki, I., 2004, "A Shape-Independent Method for Pedestrian Detection with Far-Infrared- Images," 43(5)
  5. Broggi, A., Fascioli, A., Grisleri, P., Graf, T. and Meinecke, M., 2005, "Model Based Balidation Approaches and Matching Techique for Automative Vision Based Pedestrian Detection," In Intl. IEEE Wks. on Object Tracking and Classification in and Beyond the Visible Spectrum, San Diego, USA, page in press.
  6. Mahlisch, M., Oberlandeer, M., Lahlein, O., Gavrila, D. and Ritter, W., 2005, "A Multiple Detector Approach to Low-Resolution for Pedestrian Recognition," In Proc. IEEE Intelligence Vehicles Symposium 2005, pp.23-28.
  7. Papageorgiou, C. and Poggio, T., 2000, "A Trainalbe System for Object Detection," International Journal of Computer Vision, 38(1):15-33. https://doi.org/10.1023/A:1008162616689
  8. Daugman, J. G., 1985, "Uncertainty Relation for Resolution in Space, Spatial Frequency and Orientation Optimized by Two-Dimensional Visual Cortical Filter," Journal of the Optical Society of America, 2(7):1160-1169. https://doi.org/10.1364/JOSAA.2.001160
  9. Schapire, R. E., 2002, "The Boosting Approach to Machine Learning, an Overview," In MSRI Workshop on Nonlinear Estimation and Classification.
  10. Burges, C. J. C., 1998, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, Vol. 2, pp.121-167. https://doi.org/10.1023/A:1009715923555
  11. CUDA, http://www.nvidia.com/object/cuda_home.html.
  12. GPGPU, http://gpgpu.org.
  13. Objet Oriented Scientific Computing, http://www.oonumerics.org/blitz/.

Cited by

  1. People Tracking and Accompanying Algorithm for Mobile Robot Using Kinect Sensor and Extended Kalman Filter vol.38, pp.4, 2014, https://doi.org/10.3795/KSME-A.2014.38.4.345