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Human Detection using Projected Edge Feature

Authors: Jaedo Kim, Youngjoon Han, Hernsoo Hahn


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.

Keywords: linear SVM, human detection, Projected edge descriptor, Local appearance feature

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[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.