2021
Santos, F.; Durães, D.; Marcondes, F.; Gomes, M.; Gonçalves, F.; Fonseca, J.; Wingbermuehle, J.; Machado, J.; Novais, P.
Modelling a Deep Learning Framework for Recognition of Human Actions on Video Proceedings Article
Em: A., Dzemyda G. Adeli H. Rocha (Ed.): pp. 104-112, Springer Science and Business Media Deutschland GmbH, 2021, ISSN: 21945357, (cited By 3; Conference of World Conference on Information Systems and Technologies, WorldCIST 2021 ; Conference Date: 1 April 2021 Through 2 April 2021; Conference Code:256979).
Resumo | Links | BibTeX | Etiquetas: Action recognition; Discriminative features; High-performance hardware; Human activities; Human-action recognition; Intelligent solutions; Learning frameworks; Learning models, Deep learning, Information systems; Information use; Learning systems
@inproceedings{Santos2021104,
title = {Modelling a Deep Learning Framework for Recognition of Human Actions on Video},
author = {F. Santos and D. Durães and F. Marcondes and M. Gomes and F. Gonçalves and J. Fonseca and J. Wingbermuehle and J. Machado and P. Novais},
editor = {Dzemyda G. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105949815&doi=10.1007%2f978-3-030-72657-7_10&partnerID=40&md5=63526ae46868c827835600c4dba3711b},
doi = {10.1007/978-3-030-72657-7_10},
issn = {21945357},
year = {2021},
date = {2021-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1365 AIST},
pages = {104-112},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In Human action recognition, the identification of actions is a system that can detect human activities. The types of human activity are classified into four different categories, depending on the complexity of the steps and the number of body parts involved in the action, namely gestures, actions, interactions, and activities [1]. It is challenging for video Human action recognition to capture useful and discriminative features because of the human body's variations. To obtain Intelligent Solutions for action recognition, it is necessary to training models to recognize which action is performed by a person. This paper conducted an experience on Human action recognition compare several deep learning models with a small dataset. The main goal is to obtain the same or better results than the literature, which apply a bigger dataset with the necessity of high-performance hardware. Our analysis provides a roadmap to reach the training, classification, and validation of each model. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 3; Conference of World Conference on Information Systems and Technologies, WorldCIST 2021 ; Conference Date: 1 April 2021 Through 2 April 2021; Conference Code:256979},
keywords = {Action recognition; Discriminative features; High-performance hardware; Human activities; Human-action recognition; Intelligent solutions; Learning frameworks; Learning models, Deep learning, Information systems; Information use; Learning systems},
pubstate = {published},
tppubtype = {inproceedings}
}
In Human action recognition, the identification of actions is a system that can detect human activities. The types of human activity are classified into four different categories, depending on the complexity of the steps and the number of body parts involved in the action, namely gestures, actions, interactions, and activities [1]. It is challenging for video Human action recognition to capture useful and discriminative features because of the human body's variations. To obtain Intelligent Solutions for action recognition, it is necessary to training models to recognize which action is performed by a person. This paper conducted an experience on Human action recognition compare several deep learning models with a small dataset. The main goal is to obtain the same or better results than the literature, which apply a bigger dataset with the necessity of high-performance hardware. Our analysis provides a roadmap to reach the training, classification, and validation of each model. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.