Research and Implementation of Real-Time Pedestrian Detection Algorithm

Mei Hua Xu; Huai Meng Zheng; Chen Jun Xia

doi:10.4028/www.scientific.net/AMR.945-949.1837

Paper Titles

A Study of Self-Adaptive Front Vehicle Recognition Algorithm Based on Multi-Features
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Adaptive Background Image Calculation for Video Sequences Based on Optical Flow
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Face Detection Based on the Theory of Fractal Compression
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Image Capturing and Segmentation Method for Characters Marked on Hot Billets
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Research and Implementation of Real-Time Pedestrian Detection Algorithm
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The Application of Image Processing in Medical Detection Technology
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Improved Algorithm for Wavelet Threshold Denoising and Application in Crack Image Recognition
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The Fusion Algorithm of Infrared and Visible Images Based on Computer Vision
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A Novel Vehicle Type Recognition Based on Template Matching
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 945-949Research and Implementation of Real-Time...

Research and Implementation of Real-Time Pedestrian Detection Algorithm

Abstract:

Pedestrian detection has a broad application prospect in automotive assisting driving system, but the real time performance is very poor in most common used detection methods. This paper presents a fast algorithm to realize the real-time pedestrian detection. The Local Binary Patterns (LBP) is used to describe the local texture information with the feature of less calculation, the HOG classifier to extract a typical feature of pedestrian’s edge, and then SVM to train and classify on the databases of INRIA and MIT. While scanning the images, interest regions are extracted to speed up the detection. Series of experiment results shows that the proposed pedestrian detecting strategy is effective and efficient.

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Periodical:

Advanced Materials Research (Volumes 945-949)

Pages:

1837-1841

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.945-949.1837

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Online since:

June 2014

Authors:

Mei Hua Xu*, Huai Meng Zheng, Chen Jun Xia

Keywords:

HOG, Local Binary Pattern (LBP), Pedestrian Detection, SVM

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* - Corresponding Author

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