A method of detecting human body falling action in a complex background
Dongyao Jia, Viocu Groza, Xiaohui Liu, Xu Liu, Zhu Huaihua, Qingsheng Zeng · 2016
Humans have various complex postures and movements. Considerable attention is given to the problem of recognizing a human fall. However, the recognition rates must be further improved, for practical applications, from that obtained in the previous research. In this paper, a new recognition method, based on the analysis of a human fall, is provided. Furthermore, five eigenvectors that describe a fall are defined i.e. the aspect ratio, effective area ratio, human point margin, body axis angle, and centrifugal rate of the body contour. Then, a support vector machine based on the Gauss radial basis function is trained to obtain a better identification result. The simulation results show that the model, though the combination of the five eigenvectors, has a recognition rate of 94.5%, which is a significant improvement as compared to the previous research.