본문 바로가기
  • Home

Crowd Density Estimation with Multi-class Adaboost in elevator

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2012, 17(7), pp.45-52
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

김대훈 1 이영현 1 Bonhwa Ku 1 Hanseok Ko 1

1고려대학교

Accredited

ABSTRACT

In this paper, an crowd density in elevator estimation method based on multi-class Adaboost classifier is proposed. The SOM (Self- Organizing Map) based conventional methods have shown insufficient performance in practical scenarios and have weakness for low reproducibility. The proposed method estimates the crowd density using multi-class Adaboost classifier with texture features, namely, GLDM(Grey-Level Dependency Matrix) or GGDM(Grey-Gradient Dependency Matrix). In order to classify into multi-label, weak classifier which have better performance is generated by modifying a weight update equation of general Adaboost algorithm. The crowd density is classified into four categories depending on the number of persons in the crowd, which can be 0 person, 1-2 people, 3-4 people, and 5 or more people. The experimental results under indoor environment show the proposed method improves detection rate by about 20% compared to that of the conventional method.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.