Human Detection using Projected Edge Feature

The purpose of this paper is to detect human in images. This paper proposes a method for extracting human body feature descriptors consisting of projected edge component series. The feature descriptor can express appearances and shapes of human with local and global distribution of edges. Our method evaluated with a linear SVM classifier on Daimler-Chrysler pedestrian dataset, and test with various sub-region size. The result shows that the accuracy level of proposed method similar to Histogram of Oriented Gradients(HOG) feature descriptor and feature extraction process is simple and faster than existing methods.

Authors:

• Jaedo Kim
• Youngjoon Han
• Hernsoo Hahn

Keywords:

• Human detection
• Projected edge descriptor
• Linear SVM
• Local appearance feature

References:

[1] N. Dalal, B. Triggs, and C. Schmid, "Histograms of oriented gradients for
human detection," in IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, vol. 1, 2005, pp. 886-893.
[2] T. Watanabe, S. Ito, and K. Yokoi, "Co-occurrence histograms of
oriented gradients for pedestrian detection," in 3rd Pacific Rim
Symposium on Advances in Image and Video Technology, 2009, pp.37-47.
[3] A. Shashua, Y. Gdalyahu, and G. Hayun, "Pedestrian detection for
driving assistance systems: single-frame classification and system level
performance," in IEEE Intelligent Vehicles Symposium, 2004, pp. 1-6.
[4] K. Mikolajczyk, C. Schmid, A. Zisserman, "Human detection based on a
probabilistic assembly of robust part detectors," in Pajdla, T., Matas,
J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 69-82.
[5] P. Viola, M. Jones, and D. Snow, "Detecting pedestrians using patterns of
motion and appearance," in Int. J. Computer Vision, vol. 63, no. 2, pp.
153-161.
[6] N. Dalal, B. Triggs, and C. Schmid, "Human detection using oriented
histograms of flow and appearance," in Leonardis, A., Bischof, H., Pinz,
A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428-441.
[7] S. Munder, D.M. Gavrila, "An experimental study on pedestrian
classification," in IEEE Trans. Pattern Anal. Mach. Intell, vol. 28, no. 11,
2006, pp. 1863-1868.
[8] T. Joachims, Making large-scale svm learning practical. In B. Schlkopf,
C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support
Vector Learning. The MIT Press, Cambridge, MA, USA, 1999.