Abstract
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 classi cation model, our proposed approach is able to recognize ve di erent 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.