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@article{1740896,
author = {Sedmidubský, Jan and Zezula, Pavel},
article_number = {5},
doi = {http://dx.doi.org/10.1007/s00530-021-00767-9},
keywords = {action recognition;skeleton sequence;fusion;augmentation;normalization},
language = {eng},
issn = {0942-4962},
journal = {Multimedia Systems},
title = {Efficient Combination of Classifiers for 3D Action Recognition},
url = {https://link.springer.com/article/10.1007/s00530-021-00767-9},
volume = {27},
year = {2021}
}
TY - JOUR
ID - 1740896
AU - Sedmidubský, Jan - Zezula, Pavel
PY - 2021
TI - Efficient Combination of Classifiers for 3D Action Recognition
JF - Multimedia Systems
VL - 27
IS - 5
SP - 941-952
EP - 941-952
PB - Springer
SN - 09424962
KW - action recognition;skeleton sequence;fusion;augmentation;normalization
UR - https://link.springer.com/article/10.1007/s00530-021-00767-9
N2 - The popular task of 3D human action recognition is almost exclusively solved by training deep-learning classifiers. To achieve high recognition accuracy, input 3D actions are often pre-processed by various normalization or augmentation techniques. However, it is not computationally feasible to train a classifier for each possible variant of training data in order to select the best-performing combination of pre-processing techniques for a given dataset. In this paper, we propose an evaluation procedure that determines the best combination in a very efficient way. In particular, we only train one independent classifier for each available pre-processing technique and estimate the accuracy of a specific combination by efficient fusion of the corresponding classification results based on a strict majority vote rule. In addition, for the best-ranked combination, we can retrospectively apply the normalized/augmented variants of input data to train only a single classifier. This enables to decide whether it is generally better to train a single model, or rather a set of independent classifiers whose results are fused within the classification phase. We evaluate the experiments on single-subject as well as person-interaction datasets of 3D skeleton sequences and all combinations of up to 16 normalization and augmentation techniques, some of them also proposed in this paper.
ER -
SEDMIDUBSKÝ, Jan and Pavel ZEZULA. Efficient Combination of Classifiers for 3D Action Recognition. \textit{Multimedia Systems}. Springer, 2021, vol.~27, No~5, p.~941-952. ISSN~0942-4962. Available from: https://dx.doi.org/10.1007/s00530-021-00767-9.
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