The Journal of the Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회논문지)

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

Gaze Recognition System using Random Forests in Vehicular Environment based on Smart-Phone

스마트 폰 기반 차량 환경에서의 랜덤 포레스트를 이용한 시선 인식 시스템

  • 오병훈 (성균관대학교 전자전기컴퓨터공학과) ;
  • 정광우 (한국교통대학교 철도운전시스템공학과) ;
  • 홍광석 (성균관대학교 정보통신공학부)
  • Received : 2014.11.05
  • Accepted : 2015.02.13
  • Published : 2015.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose the system which recognize the gaze using Random Forests in vehicular environment based on smart-phone. Proposed system is mainly composed of the following: face detection using Adaboost, face component estimation using Histograms, and gaze recognition based on Random Forests. We detect a driver based on the image information with a smart-phone camera, and the face component of driver is estimated. Next, we extract the feature vectors from the estimated face component and recognize gaze direction using Random Forest recognition algorithm. Also, we collected gaze database including a variety gaze direction in real environments for the experiment. In the experiment result, the face detection rate and the gaze recognition rate showed 82.02% and 84.77% average accuracies, respectively.

본 논문에서는 스마트 폰 기반 차량 환경에서의 랜덤 포레스트를 이용한 시선 인식 시스템을 제안한다. 제안한 시스템은 Adaboost 알고리즘을 이용한 얼굴 검출, 히스토그램 정보를 이용한 얼굴 구성 요소 추출, 그리고 랜덤 포레스트 알고리즘 기반의 시선 인식으로 구성되어 있다. 카메라로부터 획득한 영상정보를 바탕으로 운전자의 얼굴을 검출하고, 이를 기반으로 운전자의 얼굴 구성 요소를 추정한다. 그리고 추정된 구성 요소로부터 시선 인식에 필요한 특징 벡터를 추출하고, 랜덤 포레스트 인식 알고리즘을 이용하여 9개 방향에 대한 시선을 인식한다. 실험을 위해 실제 환경에서 다양한 시선 방향을 포함하여 DB를 수집하였으며, 실험 결과 얼굴 검출률은 약 82.02%, 시선 인식률은 약 84.77% 성능을 확인하였다.

Keywords

References

  1. SW. Kim, YG. Ji, "Driver-centered context awareness service on smart vehicle," Journal of KIISE, Vol. 29, No. 9, pp.27-34, 2011.
  2. SR. Baek, JK. Cho, JJ. Park, JH. Lim, "A Reliable Study on the Accident Reconstruction using Accident Data Recorder," The Journal of The Institute of Internet, Broadcasting and Communication(JIIBC), Vol. 14, No. 5, pp. 179-187, 2014. https://doi.org/10.7236/JIIBC.2014.14.5.179
  3. LM. Son, HY. K, "A Study on the Simulator and Trouble Prediction Monitoring Methodology of the Automotive Air Conditioner," Journal of the Korea Academia-Industrial cooperation Society(JKAIS), Vol. 14, No. 4, pp. 1568-1575, 2013. https://doi.org/10.5762/KAIS.2013.14.4.1568
  4. MB. Lee, "A Study of Factors Influencing the Intention to Use Smartphone," Journal of the Korea Industrial Information System Society, Vol. 16, No. 4, pp. 139-150, 2011.
  5. SO. Choi, YP. Kim, YS. Im, YJ. Kim, EY. Kang, "Smart Mobile Bleckbox DVR in Car Environment," The Journal of The Institute of Internet, Broadcasting and Communication (JIIBC), Vol. 13 No. 5, pp. 9-15, 2013. https://doi.org/10.7236/JIIBC.2013.13.5.9
  6. J.G. Wang and E. Sung, "Study on eye gaze estimation," IEEE Trans. System, Man, and Cybernetics-Cybernetics-Part B: Cybernetics, Vol. 32, No. 3, pp. 332-350, 2002. https://doi.org/10.1109/TSMCB.2002.999809
  7. J. Zhu and J. Yang, "Subpixel eye gaze tracking," in Proc. 5th IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 124-129, 2002.
  8. B. Hu and M.H. Qiu, "A new method for human-computer interaction by using eye gaze," in Proc. IEEE Int. Conf. System, Man, and Cybernetics, pp. 2723-2728, 1994.
  9. E. Murphy-Chutorian and M.M. Trivedi, "Head pose estimation in computer vision: A survey," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 31, No. 4, pp. 607-626, 2009. https://doi.org/10.1109/TPAMI.2008.106
  10. Paul Viola, Michael Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features", IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 511-518, 2001.
  11. M Chang, M Tekalp, and I. Sezan, "Simultaneous motion estimation and segmentation," IEEE Trans. on IP, Vol. 6, pp. 1326-1333, 1997.
  12. JH. An, BH. Oh, and KS. Hong, "Robust Fingertip Detection Algorithm for Complex Backgrounds Using AdaBoost Algorithm," Int. Conf. Computer, Communication and Information Technology(ICCCIT), pp. 104-107, 2011.
  13. Gall J, Razavi N, and van Gool I, "An Introduction to Random Forests for Multi-class Object Detection", Outdoor and Large-Scale Real-World Scene Analysis, Springer, LNCS 7474, 243-263, 2011.