Linear latent low dimensional space for online early action recognition and prediction

Recognition and prediction of human actions is one of the important tasks in various computer vision applications including video surveillance, human computer interaction and home entertainment that require online and real time approaches. In this work, we propose a novel approach that utilises continuous streams of joint motion data for recognising and predicting actions in linear latent spaces operating online and in real time. Our approach is based on supervised learning and dimensionality reduction techniques that allow the representation of high dimensional nonlinear actions to linear latent low dimensional spaces. Our methodology has been evaluated using well-known datasets and performance metrics specifically designed for online and real time action recognition and prediction. We demonstrate the performance of the proposed approach in a comparative study showing high accuracy and low latency.