Real-time activity recognition via deep learning of motion features

Abstract

Activity recognition is a challenging computer vision problem with countless applications. Here we present a real time activity recognition system using deep learning of local motion feature representations. Our approach learns to directly extract energy based motion features from video blocks. We implement the system on a distributed computing architecture and evaluate its performance on the iCub humanoid robot. We demonstrate real time performance using GPUs, paving the way for wide deployment of activity recognition systems in real world scenarios.