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Proceedings Paper

Pedestrian detection in crowded scenes with the histogram of gradients principle
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Paper Abstract

This paper describes a close to real-time scale invariant implementation of a pedestrian detector system which is based on the Histogram of Oriented Gradients (HOG) principle. Salient HOG features are first selected from a manually created very large database of samples with an evolutionary optimization procedure that directly trains a polynomial Support Vector Machine (SVM). Real-time operation is achieved by a cascaded 2-step classifier which uses first a very fast linear SVM (with the same features as the polynomial SVM) to reject most of the irrelevant detections and then computes the decision function with a polynomial SVM on the remaining set of candidate detections. Scale invariance is achieved by running the detector of constant size on scaled versions of the original input images and by clustering the results over all resolutions. The pedestrian detection system has been implemented in two versions: i) fully body detection, and ii) upper body only detection. The latter is especially suited for very busy and crowded scenarios. On a state-of-the-art PC it is able to run at a frequency of 8 - 20 frames/sec.

Paper Details

Date Published: 2 October 2006
PDF: 9 pages
Proc. SPIE 6384, Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision, 638404 (2 October 2006); doi: 10.1117/12.683441
Show Author Affiliations
O. Sidla, Joanneum Research (Austria)
M. Rosner, Joanneum Research (Austria)
Y. Lypetskyy, Joanneum Research (Austria)

Published in SPIE Proceedings Vol. 6384:
Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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