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MGA for feature weight learning in SVM —a novel optimization method in pedestrian detection

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Abstract

Pedestrian detection is a challenging task in computer vision, which is often treated as classification problem of pattern recognition. However, dealing with the high dimensional features extracted from images, it turns out to be difficult to choose and combine the informative features for classification. In this paper, a novel optimization method—Metropolis based Genetic Algorithm (MGA) is proposed to solve this problem, and a novel pedestrian detector MGA-SVM is presented and implemented. In MGA, the metropolis criterion is adopted into GA for dynamical parents’ selection, which makes the algorithm get a stronger ability to jump out of local minimum as well as achieve convergence. To test the effectiveness of the proposed MGA, we implement it for feature weight learning in SVM pedestrian detector, which is named as MGA-SVM. The experimental results demonstrate the MGA has a better optimization capacity than original GA, which leads to a more accurate pedestrian detection result by using MGA-SVM.

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Notes

  1. http://cbcl.mit.edu/software-datasets/PedestrianData.html

  2. http://pascal.inrialpes.fr/data/human/

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Authors and Affiliations

  1. The School of Software Engineering, Beijing Jiaotong University, Beijing, China

    Xiang Wei, Wei Lu, Peng Bao & Weiwei Xing

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  1. Xiang Wei
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  2. Wei Lu
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  3. Peng Bao
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  4. Weiwei Xing
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Correspondence to Wei Lu.

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Wei, X., Lu, W., Bao, P. et al. MGA for feature weight learning in SVM —a novel optimization method in pedestrian detection. Multimed Tools Appl 77, 9021–9037 (2018). https://doi.org/10.1007/s11042-017-4792-9

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