A Comparison of Few-shot Classification of Human Movement Trajectories

Lisa Gutzeit

2021

Abstract

In the active research area of human action recognition, a lot of different approaches to classify behavior have been proposed and evaluated. However, evaluations on movement recognition with a limited number of training examples, also known as Few-shot classification, are rare. In many applications, the generation of labeled training data is expensive. Manual efforts can be reduced if algorithms are used which give reliable results on small datasets. In this paper, three recognition methods are compared on gesture and stick-throwing movements of different complexity performed individually without detailed instructions in experiments in which the number of the examples used for training is limited. Movements were recorded with marker-based motion capture systems. Three classification algorithms, the Hidden Markov Model, Long Short-Term Memory network and k-Nearest Neighbor, are compared on their performance in recognition of these arm movements. The methods are evaluated regarding accuracy with limited training data, computation time and generalization to different subjects. The best results regarding training with a small number of examples and generalization are achieved with LSTM classification. The shortest calculation times are observed with k-NN classification, which shows also very good classification accuracies on data of low complexity.

Paper Citation

in Harvard Style

Gutzeit L. (2021). A Comparison of Few-shot Classification of Human Movement Trajectories.In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 243-250. DOI: 10.5220/0010244002430250

in Bibtex Style

@conference{icpram21,
author={Lisa Gutzeit},
title={A Comparison of Few-shot Classification of Human Movement Trajectories},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2021},
pages={243-250},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010244002430250},
isbn={978-989-758-486-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Comparison of Few-shot Classification of Human Movement Trajectories
SN - 978-989-758-486-2
AU - Gutzeit L.
PY - 2021
SP - 243
EP - 250
DO - 10.5220/0010244002430250