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A Novel Way of Pedestrian Detection Using Neural Network with a Weighted Fuzzy Membership Function

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Pedestrian detection is a very important part of artificial intelligence and computer vision. The normal ways to accomplish pedestrian detection include HOG (histograms of oriented gradient), Haar-like, and some other descriptors with SVM (support vector machine) or AdaBoost classifiers. Because of the lack of new classifiers and progress of neural networks on classification area, neural network can be a good classifier in the field of pedestrian detection. In this paper, we study a novel classifier NEWFM (Neural Network with a Weighted Fuzzy Membership Function) by using the HOG and Haar-like descriptors. We use the INRIA data set. We use NEWFM for the learning part and detection and compare the traditional methods of pedestrian detection to evaluate performances. The result shows that the NEWFM as new classifiers have better performance than the old ones.
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Keywords: HOG; INRIA Dataset; NEWFM; Pedestrian Detection; SVM

Document Type: Research Article

Affiliations: IT College, Gachon University, Seongnam, South Korea

Publication date: November 1, 2016

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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