Partially occluded human detection by boosting SVM

Shaopeng Tang*, Satoshi Goto

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of 2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009
ページ224-227
ページ数4
DOI
出版ステータスPublished - 2009
イベント2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009 - Kuala Lumpur
継続期間: 2009 3月 62009 3月 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

  • 計算理論と計算数学
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

フィンガープリント

「Partially occluded human detection by boosting SVM」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル