Pedestrian Detection Based on HOG-LBP Feature

In this paper, we propose a new framework in pedestrian detection by combining the HOG and uniform LBP feature on blocks. Contrast experiment result shows that detector using combined features is more powerful than one single feature. To further improve the detection performance, we make a contrast...

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Bibliographic Details
Published in2011 Seventh International Conference on Computational Intelligence and Security pp. 1184 - 1187
Main Authors Guolong Gan, Jian Cheng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2011
Subjects
Computer vision
Detectors
Feature extraction
HOG-LBP feature
Humans
Linear SVM
Pedestrian detection
Support vector machines
Training
Vectors
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Summary:In this paper, we propose a new framework in pedestrian detection by combining the HOG and uniform LBP feature on blocks. Contrast experiment result shows that detector using combined features is more powerful than one single feature. To further improve the detection performance, we make a contrast experiment that the HOG-LBP features are calculated at variable-size blocks to find the most efficient feature vector. The linear SVM is used to train the pedestrian classifier. Results presented on the INRIA dataset show that our detector is more discriminative and robust than the state-of-the-art algorithms.
ISBN:9781457720086
1457720086
DOI:10.1109/CIS.2011.262