Motion dense sampling and component clustering for action recognition

Kazuaki Aihara, Terumasa Aoki

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

In this paper, we propose Motion Dense Sampling (MDS) for action recognition, which detects very informative interest points from video frames. MDS has three advantages compared to other existing methods. The first advantage is that MDS detects only interest points which belong to action regions of all regions of a video frame. The second one is that it can detect the constant number of points even when the size of action region in an image drastically changes. The Third one is that MDS enables to describe scale invariant features by computing sampling scale for each frame based on the size of action regions. Thus, our method detects much more informative interest points from videos unlike other methods. We also propose Category Clustering and Component Clustering, which generate the very effective codebook for action recognition. Experimental results show a significant improvement over existing methods on YouTube dataset. Our method achieves 87.5 % accuracy for video classification by using only one descriptor.

Original languageEnglish
Pages (from-to)6303-6321
Number of pages19
JournalMultimedia Tools and Applications
Volume74
Issue number16
DOIs
Publication statusPublished - 2015 Aug 17

Keywords

  • Action recognition
  • Bag-of-features
  • Clustering
  • Interest point detection
  • YouTube detaset

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Motion dense sampling and component clustering for action recognition'. Together they form a unique fingerprint.

  • Cite this