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Multimedia Tools and Applications

, Volume 77, Issue 23, pp 30891–30910 | Cite as

Real-time moving pedestrian detection using contour features

  • Kai Zhao
  • Jingjing Deng
  • Deqiang Cheng
Article
  • 36 Downloads

Abstract

Pedestrian detection is one of the most fundamental research in computer vision. However, many high performance detectors run slowly. In this paper, we propose a real-time moving pedestrian detector by using efficient contour features. Firstly, the moving targets are detected by background subtraction. By combining the elliptic Fourier descriptors and the normalized central moments, we propose the Elliptic Fourier and Moments Descriptors (EFMD) to describe the moving target contours. Secondly, the moving targets are classified by the trained Support Vector Machine (SVM). In addition, we introduce a novel overlap handling algorithm based on linear fitting and normalized central moments, which improves the detection performance by reducing both false positives and miss rate. The experimental results on PETS 2009 and CAVIAR datasets show that our approach achieves a miss rate of 14% (PETS 2009) and 13% (CAVIAR) at 10−1 False Positives Per Image (FPPI) and an average runtime per frame of 30 ms (PETS 2009) and 25 ms (CAVIAR), which significantly outperforms several state-of-the-art detectors in both detection performance and runtime.

Keywords

Pedestrian detection Elliptic Fourier descriptors Normalized central moments Support vector machine Linear fit 

Notes

Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities (No.2014ZDPY32).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information and Control EngineeringChina University of Mining and TechnologyXuzhouPeople’s Republic of China

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