Fuzzy human motion analysis: a review

Chern Hong Lim, Ekta Vats, Chee Seng Chan

Research output: Contribution to journalArticleResearchpeer-review

73 Citations (Scopus)

Abstract

Human Motion Analysis (HMA) is currently one of the most popularly active research domains as such significant research interests are motivated by a number of real world applications such as video surveillance, sports analysis, healthcare monitoring and so on. However, most of these real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, the fuzzy set theory has been applied and showed great success in the recent past. In this paper, we aim at reviewing the fuzzy set oriented approaches for HMA, individuating how the fuzzy set may improve the HMA, envisaging and delineating the future perspectives. To the best of our knowledge, there is not found a single survey in the current literature that has discussed and reviewed fuzzy approaches towards the HMA. For ease of understanding, we conceptually classify the human motion into three broad levels: Low-Level (LoL), Mid-Level (MiL), and High-Level (HiL) HMA.

Original languageEnglish
Pages (from-to)1773-1796
Number of pages24
JournalPattern Recognition
Volume48
Issue number5
DOIs
Publication statusPublished - 1 May 2015
Externally publishedYes

Keywords

  • Action recognition
  • Fuzzy set theory
  • Human motion analysis

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