| Summary: | 碩士 === 國立清華大學 === 通訊工程研究所 === 100 === The recognition of human actions plays an essential role in many applications such as hu-
man machine interaction, surveillance systems, medical data analysis and etc. With the
popularity of smart televisions and handheld devices, low cost and real-time realization
of human posture recognition become important. Many researches have been proposed
for human posture and action recognition. However, most of the proposed approaches
have high computational complexity. In this paper, we propose a low-cost real-time ac-
tion recognition approach using only ve center of gravity points (COG) and four feature
sets. Two feature sets measure the displacement of the upper and lower body COG in
the vertical and horizontal directions. The other two feature sets quantize the upper
and lower body angular change rate. With these feature sets and a classication model,
our proposed approach is able to recognize ve dierent static postures including stand,
laying, bend, sit and squat and two actions, walking and handwaving. The simulation
results show that our proposed approach achieve 98.02% to 80.20% recognition rates for
various postures and actions in the KTH and ISIR databases. Our approach can achieve
real-time recognition for video sequence and has lower computational complexity than
other state-of-art algorithms.
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