Partially occluded human detection by boosting SVM

Shaopeng Tang, Satoshi Goto

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

1 Citation (Scopus)

Abstract

In this paper, a novel method to detect partially occluded humans in still images is proposed. An individual human is modeled as an assembly of natural body parts. Some part based SVM classifiers are trained first by using histogram of orientated gradient feature. Different from other boosting methods, region information is stored in each classifier. When detect human in crowed scene, according to the information of humans that have already been detected, the information of available regions could be obtained, when a new detection window is in process. In classifier sequence, the classifiers whose regions are available are selected for generating the final classifier. This method could achieve good performance on images and video sequences with several occlusions.

Original languageEnglish
Title of host publicationProceedings of 2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009
Pages224-227
Number of pages4
DOIs
Publication statusPublished - 2009
Event2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009 - Kuala Lumpur
Duration: 2009 Mar 62009 Mar 8

Other

Other2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009
CityKuala Lumpur
Period09/3/609/3/8

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Tang, S., & Goto, S. (2009). Partially occluded human detection by boosting SVM. In Proceedings of 2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009 (pp. 224-227). [5069221] https://doi.org/10.1109/CSPA.2009.5069221