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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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