Crowd density estimation using multi-class adaboost

Daehum Kim, Younghyun Lee, Bonhwa Ku, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this paper, we propose a crowd density estimation algorithm based on multi-class Adaboost using spectral texture features. Conventional methods based on self-organizing maps have shown unsatisfactory performance in practical scenarios, and in particular, they have exhibited abrupt degradation in performance under special conditions of crowd densities. In order to address these problems, we have developed a new training strategy by incorporating multi-class Adaboost with spectral texture features that represent a global texture pattern. According to the representative experimental results, the proposed method shows an average improvement of about 30% in the correct recognition rate, as compared to existing conventional methods.

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012
Pages447-451
Number of pages5
DOIs
Publication statusPublished - 2012
Event2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012 - Beijing, China
Duration: 2012 Sep 182012 Sep 21

Publication series

NameProceedings - 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012

Other

Other2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012
CountryChina
CityBeijing
Period12/9/1812/9/21

Keywords

  • Crowd densitiy estimation
  • Multi-class adaboost

ASJC Scopus subject areas

  • Computer Networks and Communications

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  • Cite this

    Kim, D., Lee, Y., Ku, B., & Ko, H. (2012). Crowd density estimation using multi-class adaboost. In Proceedings - 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012 (pp. 447-451). [6328055] (Proceedings - 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012). https://doi.org/10.1109/AVSS.2012.31