Kinematic features for human action recognition using Restricted Boltzmann Machines

Human Action recognition research is an interesting and active field of research in the current years. Human Action Recognition (HAR) has many potential and promising applications, in such fields as security, surveillance, professional sports, and human computer interaction. One of the ongoing challenges of HAR is the creation of methods that are subject invariant, that is methods of action recognition that is not influenced by the appearance of the object in question. One the methods that has been quite popular in recent years are methods that are based on the kinematic features of the action to be recognized. Such feature are based on the optical flow of the pixel in time, and include features such as the divergence, curl (vorticity) among others. These features are proven to be subject invariant and can easily be calculated in a frame by frame basis. In this study, we present an analysis of feature classification techniques for action classification based on such kinematic features. In this paper, we build a multilayer neural network model trained using Restricted Boltzmann Machines (RBM) that achieves 70% cross validation accuracy on the Weizmann dataset using kinematic features.