Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Edge-based Method for Human Detection in an Image

영상 내 사람의 검출을 위한 에지 기반 방법

  • Do, Yongtae (Division of Electronic & Electrical Engineering, Daegu University) ;
  • Ban, Jonghee (Department of Information & Communication Engineering, Graduate School, Daegu University)
  • 도용태 (대구대학교 전자전기공학부) ;
  • 반종희 (대구대학교 대학원 정보통신공학과)
  • Received : 2016.07.22
  • Accepted : 2016.07.28
  • Published : 2016.07.31
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Abstract

Human sensing is an important but challenging technology. Unlike other methods for sensing humans, a vision sensor has many advantages, and there has been active research in automatic human detection in camera images. The combination of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is currently one of the most successful methods in vision-based human detection. However, extracting HOG features from an image is computer intensive, and it is thus hard to employ the HOG method in real-time processing applications. This paper describes an efficient solution to this speed problem of the HOG method. Our method obtains edge information of an image and finds candidate regions where humans very likely exist based on the distribution pattern of the detected edge points. The HOG features are then extracted only from the candidate image regions. Since complex HOG processing is adaptively done by the guidance of the simpler edge detection step, human detection can be performed quickly. Experimental results show that the proposed method is effective in various images.

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References

  1. S. Y. Kwon, "Effect of P (VDF/TrFE) film thickness on the characteristics of pyroelectric passive infrared ray sensor for human body detection", J. Sensor Sci. & Tech., Vol. 20, No. 2, pp. 114-117, 2011. https://doi.org/10.5369/JSST.2011.20.2.114
  2. J. R. Choi, "Pyroelectric infrared microsensors made for human body detection", J. Sensor Sci. & Tech., Vol. 7, No. 2, pp. 21-28, 1998.
  3. A. Lipton, H. Fujiyoshi and R.S. Patil, "Moving target classification and tracking from real-time video", Proc. IEEE Workshop Applications of Computer Vision, pp. 8-14, 1998.
  4. H. Fujiyoshi and A. Lipton, "Real-time human motion analysis by image skeletonization", Proc. IEEE Workshop Applications of Computer Vision, pp. 15-21, 1998.
  5. Y. Do and T. Kanade, "Counting people from image sequences", Proc. Int. Conf. on Imaging Sci., System & Tech., Vol. 1, pp. 185-190, 2000.
  6. D. M. Gavrila and J. Giebel, "Shape-based pedestrian detection and tracking", Proc. IEEE Intelligent Vehicle Symposium, pp. 8-14, 2002.
  7. T. N. Duc, P. Ogunbona and W. Li, "Human detection based on weighted template matching", Proc. IEEE Conference on Multimedia and Expo, pp. 634-637, 2009.
  8. N. Dalal and B. Triggs. "Histograms of oriented gradients for human detection", Proc. Computer Vision and Pattern Recognition, pp. 886-893, 2005.
  9. V. Vapnik and A. Lerner, "Pattern recognition using generalized portrait method", Automation and Remote Control, 24, 774-780, 1963.
  10. G. Xu, X. Wu, L. Liu and Z. Wu, "Real-time pedestrian detection based on edge factor and Histogram of Oriented Gradient", Proc. IEEE Conference on Information and Automation, pp. 384-389, 2011.
  11. http://cbcl.mit.edu/cbcl/software-datasets/PedestrianData.html (Retrieved on May 30, 2016).