Fuzzy human motion analysis: A review

By Chern Hong Lim, Ekta Vats, Chee Seng Chan. 2014

91 citations recorded by the source corpus.

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. HighlightsA survey of fuzzy set oriented methods for human motion analysis is presented.This is the first time such a survey is presented in the fuzzy set literature.Categorization of existing approaches into three broad levels is performed.Insights and suggestions for future research are discussed.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

3D skeleton-based human action classification: A survey (2016, background): “Therefore, there is a vast literature, including interesting surveys [13 – 19], on human body pose estimation [20 – 24] and activity/action classi ﬁ cation [25 – 30].”