INTEGRATION OF GENERATIVE LEARNING AND MULTIPLE POSE CLASSIFIERS FOR PEDESTRIAN DETECTION

Hidefumi Yoshida, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Kunihiro Goto, Yoshikatsu Kimura, Takashi Naito

Abstract

Recently, pedestrian detection from in-vehicle camera images is becoming an important technology in ITS (Intelligent Transportation System). However, it is difficult to detect pedestrians stably due to the variety of their poses and their backgrounds. To tackle this problem, we propose a method to detect various pedestrians from in-vehicle camera images by using multiple classifiers corresponding to various pedestrian pose classes. Since pedestrians’ pose varies widely, it is difficult to construct a single classifier that can detect pedestrians with various poses stably. Therefore, this paper constructs multiple classifiers optimized for variously posed pedestrians by classifying pedestrian images into multiple pose classes. Also, to reduce the bias and the cost for preparing numerous pedestrian images for each pose class for learning, the proposed method employs a generative learning method. Finally, the proposed method constructs multiple classifiers by using the synthesized pedestrian images. Experimental results showed that the detection accuracy of the proposed method outperformed comparative methods, and we confirmed that the proposed method could detect variously posed pedestrians stably.

References

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Paper Citation


in Harvard Style

Yoshida H., Deguchi D., Ide I., Murase H., Goto K., Kimura Y. and Naito T. (2012). INTEGRATION OF GENERATIVE LEARNING AND MULTIPLE POSE CLASSIFIERS FOR PEDESTRIAN DETECTION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 567-572. DOI: 10.5220/0003817305670572


in Bibtex Style

@conference{visapp12,
author={Hidefumi Yoshida and Daisuke Deguchi and Ichiro Ide and Hiroshi Murase and Kunihiro Goto and Yoshikatsu Kimura and Takashi Naito},
title={INTEGRATION OF GENERATIVE LEARNING AND MULTIPLE POSE CLASSIFIERS FOR PEDESTRIAN DETECTION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={567-572},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003817305670572},
isbn={978-989-8565-03-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - INTEGRATION OF GENERATIVE LEARNING AND MULTIPLE POSE CLASSIFIERS FOR PEDESTRIAN DETECTION
SN - 978-989-8565-03-7
AU - Yoshida H.
AU - Deguchi D.
AU - Ide I.
AU - Murase H.
AU - Goto K.
AU - Kimura Y.
AU - Naito T.
PY - 2012
SP - 567
EP - 572
DO - 10.5220/0003817305670572