Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks

Kyunghyun Cho, Xi Chen

2014

Abstract

The gesture recognition using motion capture data and depth sensors has recently drawn more attention in vision recognition. Currently most systems only classify dataset with a couple of dozens different actions. Moreover, feature extraction from the data is often computational complex. In this paper, we propose a novel system to recognize the actions from skeleton data with simple, but effective, features using deep neural networks. Features are extracted for each frame based on the relative positions of joints (PO), temporal differences (TD), and normalized trajectories of motion (NT). Given these features a hybrid multi-layer perceptron is trained, which simultaneously classifies and reconstructs input data. We use deep autoencoder to visualize learnt features. The experiments show that deep neural networks can capture more discriminative information than, for instance, principal component analysis can. We test our system on a public database with 65 classes and more than 2,000 motion sequences. We obtain an accuracy above 95% which is, to our knowledge, the state of the art result for such a large dataset.

Paper Citation

in Harvard Style

Cho K. and Chen X. (2014). Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 122-130. DOI: 10.5220/0004718301220130

in Bibtex Style

@conference{visapp14,
author={Kyunghyun Cho and Xi Chen},
title={Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={122-130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004718301220130},
isbn={978-989-758-004-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks
SN - 978-989-758-004-8
AU - Cho K.
AU - Chen X.
PY - 2014
SP - 122
EP - 130
DO - 10.5220/0004718301220130