Learning Human Motion Inference with 3D Convolutional Neural Network

抄録

In this paper, we present an end-to-end learning model for human motion inference from 3D point cloud data. Examples of human motion to be learned are collected as point cloud data through a 3D sensor, mapped into 3D occupancy grids and then used as supervised learning samples for a 3D Convolutional Neural Network (3D CNN). The 3D CNN model is able to learn spatiotemporal features from time steps of occupancy grids and classify human motion inferences with an accuracy of up to 84% within 60% of the motion performed. We demonstrate the performance of this model in real time by predicting the intention of a human arm motion for some predetermined targets and furthermore generalising to new users whose data were not used for training of the model. This method will be useful for human-robot collaboration and human-computer interaction applications that need human motion analysis.