Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Exploiting Color Segmentation in Pedestrian Upper-body Detection

보행자 상반신 검출에서의 컬러 세그먼테이션 활용

  • Park, Lae-Jeong (Department of Electronic Engineering, Gangneung-Wonju National University)
  • 박래정 (강릉원주대학교 전자공학과)
  • Received : 2014.08.13
  • Accepted : 2014.11.01
  • Published : 2014.11.25
Download PDF
⟨ Previous Next ⟩

Abstract

The paper proposes a new method of segmentation-based feature extraction to improve performance in pedestrian upper-body detection. General pedestrian detectors that use local features are often plagued by false positives due to the locality. Color information of multi parts of the upper body is utilized in figure-ground segmentation scheme to extract an salient, "global" shape feature capable of reducing the false positives. The performance of the multi-part color segmentation-based feature is evaluated by changing color spaces and the parameters of color histogram. The experimental result from an upper-body dataset shows that the proposed feature is effective in reducing the false positives of local feature-based detectors.

본 논문에서는 보행자 상반신 검출기의 성능을 향상하기 위한 세그먼테이션에 기반한 특징 추출 방법을 제안한다. 상반신의 부분별 색상 분포를 활용한 멀티 파트 컬러 세그먼테이션을 사용하여 국소 특징이 갖는 한계로 인해 발생하는 오검출의 감소에 효과적인 "전역적" 윤곽 특징을 추출한다. 컬러 공간과 히스토그램 분해도에 따른 성능을 분석하였으며, 자체 구축한 보행자 상반신 영상을 사용한 실험을 통해서 제안한 방법으로 추출한 특징이 국소 특징 기반 검출기의 오검출 감소에 효과적임을 확인하였다.

Keywords

References

  1. D. Geronimo, A. M. Lopez, A. D. Sappa, and T. Graf, "Survey on pedestrian detection for advanced driver assistance systems," IEEE Trans. on PAMI, vol. 32, pp. 1239-1258, 2010. https://doi.org/10.1109/TPAMI.2009.122
  2. Q. Zhu, M.-C. Yeh, K.-T. Cheng, and S. Avidan, "Fast human detection using a cascade of histograms of oriented gradients," Proc. IEEE Conf. on CVPR, pp. 1491-1498, 2006.
  3. M. Li, Z. Zhang, K. Huang, and T. Tan, "Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection," Proc. Int. Conf. on Pattern Recognition, pp. 1-4, 2008.
  4. X. Ding, H. Xu, P. Cui, L. Sun, and S. Yang, "A cascade SVM approach for head-shoulder detection using histograms of oriented gradients," Proc. IEEE Int. Symposium on Circuits and Systems, pp. 1791-1794, 2009.
  5. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," Proc. IEEE Conf. on CVPR, pp. 886-893, 2005.
  6. S. Walk, N. Majer, K. Schindler, and B. Schiele, "New features and insights for pedestrian detection," Proc. IEEE Conf. on CVPR, pp. 1030-1037, 2010.
  7. L.-J. Park and J-H Moon, "Exploiting global self similarity for head-shoulder detection," World Academy of Science, Engineering and Technology, vol. 76, pp. 144-148, 2013.
  8. D. Ramanan, "Using segmentation to verify object hypothesis," Proc. IEEE Conf. on CVPR, pp. 1-8, 2007.
  9. L.-J. Park, "Reduction of false positives in pedestrian detection by color segmentation," Proc. of KIIS Spring Conference, pp. 157-158, 2013.
  10. Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," Proc. IEEE Conf. on Computer Vision, pp. 377-384, 1999.
  11. T. Gevers, A. Gijsenij, J. van de Weijer, and J-M. Geusebroek, Color in Computer Vision: Fundamentals and Applications, Wiley, 2012.