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An Improved HOG Based Pedestrian Detector

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)

Abstract

Despite being widely adopted and rigorously followed in many successful pedestrian detectors, the original HOG (Histogram of Oriented Gradients) descriptor are in fact NOT optimally tuned for pedestrian detection. To address this issue, we quantitatively investigate the interplay among different HOG parameters, in particular that among the cell size, aspect ratio, and detection window size, which makes it possible to jointly tune these parameters to achieve better pedestrian detection performance. In addition, we extend the training procedure of the original HOG-based detector of Dalal et al. through presenting an automatic positive sample generation algorithm, introducing LSVM (Latent SVM) to iteratively optimize the positive training samples, and adopting a hard negative mining method. To verify the effectiveness of our improved detector, we conduct extensive experiments on INRIA Person, TUD-Brussels and Caltech Pedestrians datasets. On all these datasets, our detector outperforms significantly the original HOG detector of Dalal et al.

Keywords

Pedestrian detection HOG parameter tuning Latent SVM Full image evaluation 

Notes

Acknowledgments

This work was supported by the National Science Foundation of China (NSFC) under grant number 60902091.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.College of Information Systems and ManagementNational University of Defense TechnologyChangshaChina

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