Dominant Codewords Selection with Topic Model for Action Recognition

Hirokatsu Kataoka, Kenji Iwata, Yutaka Satoh, Masaki Hayashi, Yoshimitsu Aoki, Slobodan Ilic

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this paper, we propose a framework for recognizing human activities that uses only in-topic dominant codewords and a mixture of intertopic vectors. Latent Dirichlet allocation (LDA) is used to develop approximations of human motion primitives, these are mid-level representations, and they adaptively integrate dominant vectors when classifying human activities. In LDA topic modeling, action videos (documents) are represented by a bag-of-words (input from a dictionary), and these are based on improved dense trajectories ([18]). The output topics correspond to human motion primitives, such as finger moving or subtle leg motion. We eliminate the impurities, such as missed tracking or changing light conditions, in each motion primitive. The assembled vector of motion primitives is an improved representation of the action. We demonstrate our method on four different datasets.

本文言語English
ホスト出版物のタイトルProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
出版社IEEE Computer Society
ページ770-777
ページ数8
ISBN(電子版)9781467388504
DOI
出版ステータスPublished - 2016 12月 16
イベント29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 - Las Vegas, United States
継続期間: 2016 6月 262016 7月 1

Other

Other29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
国/地域United States
CityLas Vegas
Period16/6/2616/7/1

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学

フィンガープリント

「Dominant Codewords Selection with Topic Model for Action Recognition」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