Fuzzy human motion analysis: A review
This is an incremental contribution as it provides the first survey on fuzzy approaches for HMA, which could benefit researchers in fields such as computer vision and robotics.
The paper reviews fuzzy set theory approaches for Human Motion Analysis (HMA), addressing uncertainties in real-world applications like video surveillance and healthcare, and finds that fuzzy methods have shown great success in improving HMA.
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.