Efficient Combination of Classifiers for 3D Action Recognition

J 2021

Efficient Combination of Classifiers for 3D Action Recognition

SEDMIDUBSKÝ, Jan a Pavel ZEZULA

Základní údaje

Originální název

Efficient Combination of Classifiers for 3D Action Recognition

Autoři

SEDMIDUBSKÝ, Jan (203 Česká republika, garant, domácí) a Pavel ZEZULA (203 Česká republika, domácí)

Vydání

Multimedia Systems, Springer, 2021, 0942-4962

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 2.603

Kód RIV

RIV/00216224:14330/21:00118857

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.1007/s00530-021-00767-9

UT WoS

000628724200001

Klíčová slova anglicky

action recognition;skeleton sequence;fusion;augmentation;normalization

Štítky

DISA

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 19. 4. 2022 21:42, doc. RNDr. Jan Sedmidubský, Ph.D.

Anotace

V originále

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.

Návaznosti

GA19-02033S, projekt VaV

Název: Vyhledávání, analytika a anotace datových toků lidských pohybů

Investor: Grantová agentura ČR, Searching, Mining, and Annotating Human Motion Streams

Citovat

SEDMIDUBSKÝ, Jan a Pavel ZEZULA. Efficient Combination of Classifiers for 3D Action Recognition. Multimedia Systems. Springer, 2021, roč. 27, č. 5, s. 941-952. ISSN 0942-4962. Dostupné z: https://dx.doi.org/10.1007/s00530-021-00767-9.

@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 a Pavel ZEZULA. Efficient Combination of Classifiers for 3D Action Recognition. \textit{Multimedia Systems}. Springer, 2021, roč.~27, č.~5, s.~941-952. ISSN~0942-4962. Dostupné z: https://dx.doi.org/10.1007/s00530-021-00767-9.

Podrobný výpis o publikaci