Detection of Pedestrians Based on the Fusion of Human Characteristics and Kernel Density Estimation

  • Shi Cheng
  • Muyan Zhou
  • Chunhong Lu
  • Yuanjin LiEmail author
  • Zelin Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 986)


The kernel density estimate does not need to have the characteristic distribution hypothesis to the background, it also does not require the estimation parameter, and it can deal with the moving target detection under the complex background, but the kernel function bandwidth choice uniformly puzzles the algorithm application. To solve this problem, this paper proposes a fusion method of human body characteristics and kernel density estimation for pedestrian detection. Firstly, the kernel function bandwidth is chosen by the prior information of moving target, then the foreground (moving target) is extracted based on kernel density estimation, finally, using human features to detect video pedestrians. The experimental results show that the calculation of kernel density estimation is reduced by comparing introduction of prior information with traditional methods, and the pedestrian and no pedestrian can be detected accurately by the interference of light variation and noise.


Moving target Priori information Kernel density estimation Pedestrians 



This project is supported by Anhui University Natural Science Research Project (No. KJ2018A0431), Jiangsu Modern Educational Technology Research Project (No. 2017-R-54131), Nantong Science and Technology Project (No. MS12016036), Research on Teaching Reform at Nantong University (No. 2018043).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Shi Cheng
    • 1
  • Muyan Zhou
    • 1
  • Chunhong Lu
    • 1
  • Yuanjin Li
    • 2
    Email author
  • Zelin Wang
    • 1
  1. 1.School of Computer Science and TechnologyNantong UniversityNantongChina
  2. 2.School of Computer and Information EngineeringChuzhou UniversityChuzhouChina

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