Classifying actions based on histogram of oriented velocity vectors

Somar Boubou, Einoshin Suzuki

研究成果: Contribution to journalArticle査読

23 被引用数 (Scopus)

抄録

We present a new descriptor for activity recognition from skeleton data acquired by Kinect. Previous approaches tend to employ complex descriptors which require extensively long computation time. In this study, we present an efficient and effective descriptor which we name as Histogram-of-Oriented-Velocity-Vectors (HOVV). It is a scale-invariant, speed-invariant and length-invariant descriptor for human actions represented by 3D skeletons acquired by Kinect. We describe the skeleton sequence using 2D spatial histogram capturing the distribution of the orientations of velocity vectors of the joint in a spherical coordinate system. We make use of three methods to classify actions represented by HOVV descriptor. These are k-nearest neighbour classifier, Support Vector Machines classifier and Extreme Learning Machines. For the cases when HOVV descriptor is not sufficient, such as to differentiate actions which involve tiny movement of joints such as “sit-still”, we also incorporate a simple skeleton descriptor as a prior to the action descriptor. Through extensive experiments, we test our system with different configurations. We also demonstrate that our HOVV descriptor outperforms the state-of-the-art methods. The results demonstrate that our descriptor has much shorter computational time due to the simpler computations needed for feature extraction. Moreover our descriptor shows a higher recognition accuracy compared with the state-of-the-art methods.

本文言語英語
ページ(範囲)49-65
ページ数17
ジャーナルJournal of Intelligent Information Systems
44
1
DOI
出版ステータス出版済み - 2 2014

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 情報システム
  • ハードウェアとアーキテクチャ
  • コンピュータ ネットワークおよび通信
  • 人工知能

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