JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Estimating Human Size in 2D Image for Improvement of Detection Speed in Indoor Environments
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Journal of Broadcast Engineering
  • Volume 21, Issue 2,  2016, pp.252-260
  • Publisher : The Korean Institute of Broadcast and Media Engineers
  • DOI : 10.5909/JBE.2016.21.2.252
 Title & Authors
Estimating Human Size in 2D Image for Improvement of Detection Speed in Indoor Environments
Gil, Jong In; Kim, Manbae;
  PDF(new window)
 Abstract
The performance of human detection system is affected by camera location and view angle. In 2D image acquired from such camera settings, humans are displayed in different sizes. Detecting all the humans with diverse sizes poses a difficulty in realizing a real-time system. However, if the size of a human in an image can be predicted, the processing time of human detection would be greatly reduced. In this paper, we propose a method that estimates human size by constructing an indoor scene in 3D space. Since the human has constant size everywhere in 3D space, it is possible to estimate accurate human size in 2D image by projecting 3D human into the image space. Experimental results validate that a human size can be predicted from the proposed method and that machine-learning based detection methods can yield the reduction of the processing time.
 Keywords
human size estimation;camera calibration;image projection;depth map;
 Language
Korean
 Cited by
 References
1.
V. A. Topkar, A. K. Sood and B. Kjell, ″Object Detection Using Contrast Based Scale-space,″in Proc, IEEE Conf. Computer Vision and Pattern Recognition, pp. 700-701, June 1999.

2.
P. Dollar, R. Appel, S. Belongie and P. Perona, ″Fast Feature Pyramids for Object Detection,″ IEEE Trans, Pattern Analysis and Machine Intellingence, Vol. 36, No. 8, pp. 1532-1545, Aug. 2014. crossref(new window)

3.
N. He, J. Cao and L. Song, ″Scale Space Histogram of Oriented Gradients for Human Detection,″ IEEE Intl. Symposium on Information Science and Engineering, pp. 167-170, Dec. 2008.

4.
M. Park, N. Moon, S. Ryu, J. Kong, Y. Lee and W. Mun, ″A Pixel-Weighting Method for Discriminating Objects of Different Sizes in an Image Captured from a Single Camera,″ in Proc. IEEE 3rd Canadian Conf. Computer and Robot Vision, pp. 36-36, 2006.

5.
I. Kispal and E. Jeges, ″Human Height Estimation Using a Calibrated Camera,″in Proc, CVPR, 2008.

6.
Y. Li, B. Wu and R. Nevatia, ″Human Detection by Searching in 3D Space Using Camera and Scene Knowledge,″IEEE 19th Intl. Conf. Pattern Recognition, pp. 1-5, Dec. 2008.

7.
C. Zeng and H. Ma, ″Human Detection Using Multi-camera and 3D Scene Knowledge,″ IEEE 18th Intl. Conf. Image Processing, pp. 1793-1796, Sep. 2011.

8.
P. Cignono, C. Montani and R. Scopigno, ″DeWall: A fast divide and conquer Delaunay triangulation algorithm,″ in Computer-Aided Design, 30(5), 1988, pp, 333-341, 1980. crossref(new window)

9.
H. Gouraud, ″Continuous shading of curved surfaces,″IEEE Trans. Computer, Vol. C-20, Issue. 6, pp. 623-629, 1971. crossref(new window)

10.
S. Choi, J. Park, J. Byun and W. Yu, ″Robust ground plane detection from 3D point clouds,″ IEEE 14th Intl, Conf. Control, Automation and System, pp. 1076-1081, Oct. 2014.

11.
J. Arrospide, L. Salgado, M. Nieto and R. Mohedano, ″Homography-based ground plane detection using a single on-board camer a,″IEEE Intelligent Transport Systems, Vol 4, Issue 2, pp. 149-160, June 2010. crossref(new window)

12.
Z. Zhang, “A Flexible New Technique for Camera Calibration,″ IEEE Trans. Pattern Analysis and Machine Intellingence, Vol 22, No 11, pp. 1330-1334, Nov. 2000. crossref(new window)

13.
J. Moore, ″The Levenberg-Marquardt Algorithm, Implementation, and Theory,″Numerical Analysis, G.A. Watson, ed., Springer-Verlag, 1977.

14.
http://graphics.cs.msu.ru/en/node/909

15.
C. Chang and C. Lin,″LIBSVM :a library for support vector machines,″ ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011. crossref(new window)

16.
P. Viola and M. Jones,″Robust Real-time Object Detection,″In Proc. 2nd Int'l Workshop on Statistical and Computational Theories of Vision -Modeling, Learning, Computing and Sampling, Vancouver, Canada, July 2001.

17.
N. Dalal and B. Triggs,″Histograms of oriented gradients for human detection″, IEEE Conference on Computer Vision and Pattern Recognition, Vol 1, pp. 886-893, 2005.

18.
Ojala. T, Pietikainen, M and Harwood. D, ″A Comparative Study on Texture Measures with Classification Based on Feature Distributions,″Pattern Recognition, 29(1), pp. 51-59, 1996. crossref(new window)