Fuzzy human motion analysis: A review

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.Comment: Accepted in Pattern Recognition, first survey paper that discusses and reviews fuzzy approaches towards HM

IFSA-EUSFLAT 2009 Fuzzy Voxel Object

Abstract—In this paper, computer vision and fuzzy set theory are merged for the robust construction of three-dimensional objects using a small number of cameras and minimal a priori knowledge about the objects. This work extends our previously defined crisp model, which has been successfully used for recognizing and linguistically summarizing human activity. The objects true features more closely resemble the fuzzy object than those of the crisp object. This is demonstrated both empirically and through the comparison of features used in tracking human activity. Keywords—computer vision, human activity analysis, fuzzy objects, fuzzy voxel person.