Automatic extraction of semantic features for real-time action recognition using depth architecture networks

Motion analysis automatically captures, recognizes and predicts ongoing human activities, which can be widely applied to various useful domains such as security surveillance in public spaces, including shopping centers and airports. With the development of the technologies like 3D specialized markers, we could capture the moving signals from marker joints and create a huge set of 3D motion capture (MOCAP) data. We propose in this work a method to automatically extract the action features which can be used for action recognition. We create an depth architecture model by combining multilevel networks which can focus on the recognizing objects in detail. These networks can learn the extracted features and perform action recognition. This propose model not only can extract the semantic action features from 3D MOCAP data, but also can apply for the real-time action recognition.