Efficient Combination of Classifiers for 3D Action Recognition

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00118857" target="_blank" >RIV/00216224:14330/21:00118857 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s00530-021-00767-9" target="_blank" >https://link.springer.com/article/10.1007/s00530-021-00767-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00530-021-00767-9" target="_blank" >10.1007/s00530-021-00767-9</a>

Result language

angličtina

Original language name

Efficient Combination of Classifiers for 3D Action Recognition

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10200 - Computer and information sciences

Project

<a href="/en/project/GA19-02033S" target="_blank" >GA19-02033S: Searching, Mining, and Annotating Human Motion Streams</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Multimedia Systems

ISSN

0942-4962

e-ISSN

1432-1882

Volume of the periodical

27

Issue of the periodical within the volume

5

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

941-952

UT code for WoS article

000628724200001

EID of the result in the Scopus database

2-s2.0-85102599920