Towards multi-person motion forecasting
Yasuo Katsuhara, Hirotaka Kaji · 2019
Forecasting body motion has a lot of potential applications such as sports and entertainment. Previous studies have mainly employed cameras and optical motion captures to measure the joint positions of person, and predicted them about 0.5 seconds before by using deep neural networks. However, following two difficulties have to be solved to install the forecasting system into the real world: One is that camera and optical based methods have to take into account the environmental settings and occlusion problems, and the other is that previous studies have not considered plural persons. In this paper, we propose a multi-person motion forecasting system by using inertial measurement unit (IMU) motion captures to overcome these difficulties simultaneously, and demonstrate a preliminary result.