A review on computer vision-based methods for human action recognition

Mahmoud Al-Faris*, John Chiverton, David Ndzi, Ahmed Isam Ahmed

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Human action recognition targets recognising different actions from a sequence of observations and different environmental conditions. A wide different applications is applicable to vision based action recognition research. This can include video surveillance, tracking, health care, and human–computer interaction. However, accurate and effective vision based recognition systems continue to be a big challenging area of research in the field of computer vision. This review introduces the most recent human action recognition systems and provides the advances of state-of-the-art methods. To this end, the direction of this research is sorted out from hand-crafted representation based methods including holistic and local representation methods with various sources of data, to a deep learning technology including discriminative and generative models and multi-modality based methods. Next, the most common datasets of human action recognition are presented. This review introduces several analyses, comparisons and recommendations that help to find out the direction of future research.
Original languageEnglish
Number of pages32
JournalJournal of Imaging
Volume6
Issue number6
DOIs
Publication statusPublished - 10 Jun 2020

Keywords

  • human action recognition
  • hand-crafted feature
  • deep learning
  • feature representation

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