Human Motion Classification
Using LSTM for Automatic Classification of Human Motion Capture Data
Authors: RogeĢrio da Silva, Aljosa Smolic, Jan Ondrej
Presented on 25th February 2019 at the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
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Creative studios tend to produce an overwhelming amount of content everyday and being able to manage these data and reuse it in new productions represent a way for reducing costs and increasing productivity and profit. This work is part of a project aiming to develop reusable assets in creative productions. This paper describes our first attempt using deep learning to classify human motion from motion capture files. It relies on a long short-term memory network (LSTM) trained to recognize action on a simplified ontology of basic actions like walking, running or jumping. Our solution was able of recognizing several actions with an accuracy over 95% in the best cases.