Human Activity Recognition with Capsule Networks.

Human activity recognition is a challenging problem, where deep learning methods are showing to be very efficient. In this paper we propose the use of capsule networks. This type of networks have proved to generalize better to novel viewpoints than convolutional neural networks. We show that the use of capsule networks into a straightforward architecture, between a convolutional preprocessing stage to extract visual features and a header for carrying out the task, is able to attain competitive results with spatio-temporal data without the use of any kind of recurrent neural network. Moreover, an analysis of the obtained results shows that our architecture is capable of learning the properties that encode the spatio-temporal dynamics of the movements that characterize each activity.