Pedestrian Detection with Improved LBP and Hog Algorithm

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DOI: 10.4236/oalib.1104573    698 Downloads   2,301 Views  Citations
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ABSTRACT

This article aims to improve the HOG SVM pedestrian detection method proposed by previous researchers. The speed of HOG SVM to detect pedestrians is relatively slow, and the detection accuracy is not very good. This paper proposes a PCA (principal component analysis) dimension reduction for HOG and also interpolates it. The article combines the dimensions of individual HOG features and improves their accuracy, and fuses them with improved LBP features. The features of the fusion of HOG features and LBP features can both express pedestrian profile information and obtain pedestrian texture information. This can improve the speed of pedestrian detection and improve the accuracy of detection, which is beneficial to reduce false detection and missed detection. Although some researchers have combined the two features of HOG and LBP, after simple fusion of these two features, the experimental results show that the detection effect is not much improved. This article is aimed at different formats of video detection material, an application program written on the MFC platform, making pedestrian detection of the material quickly verified, which is conducive to pedestrian detection results data analysis and recording.

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Zhou, W. and Luo, S. (2018) Pedestrian Detection with Improved LBP and Hog Algorithm. Open Access Library Journal, 5, 1-10. doi: 10.4236/oalib.1104573.
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