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Fast multi-feature pedestrian detection algorithm based on histogram of oriented gradient using discrete wavelet transform

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Abstract

A convergence between a natural user interface (NUI) and advanced driver assistance system is considered as a next generation technology. This kind of interfacing system technology becomes more popular in driver assistance system of automobile. Especially, pedestrian detection is an important cue for intelligent vehicles and interactive driver assistance system. In this paper, we propose a pedestrian detection feature and technique by combining histogram of the oriented gradient (HOG) and discrete wavelet transform (DWT). In the method, the magnitude of motion is used to set region of interest (ROI) for improving detection speed. Then, we employ multi-feature for a pedestrian detection based on the HOG and DWT. In last stage, to classify whether a candidate window contains a pedestrian or not, the designed multi-feature is learned by using the training data with the support vector machine (SVM) mechanism. Experimental results show that the proposed algorithm increases the speed-up factor of 27.21 % by comparing to the existing method using the original HOG feature.

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Acknowledgments

This work was supported by the agency specific research program of MSIP, Korea [Development of multi-sensor platform technology for context cognitive smart-car]

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Authors and Affiliations

  1. Department of Computer Engineering, SunMoon University, Asan, South Korea

    Gwang-Soo Hong, Byung-Gyu Kim & Young-Sup Hwang

  2. Automotive IT Platform Research Team, ETRI, Daegu, South Korea

    Kee-Koo Kwon

Authors
  1. Gwang-Soo Hong
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  2. Byung-Gyu Kim
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  3. Young-Sup Hwang
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  4. Kee-Koo Kwon
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Correspondence to Byung-Gyu Kim.

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Hong, GS., Kim, BG., Hwang, YS. et al. Fast multi-feature pedestrian detection algorithm based on histogram of oriented gradient using discrete wavelet transform. Multimed Tools Appl 75, 15229–15245 (2016). https://doi.org/10.1007/s11042-015-2455-2

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  • DOI: https://doi.org/10.1007/s11042-015-2455-2

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