Transfer learning with fine tuning for human action recognition from still images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50018494" target="_blank" >RIV/62690094:18450/21:50018494 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s11042-021-10753-y" target="_blank" >https://link.springer.com/article/10.1007/s11042-021-10753-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11042-021-10753-y" target="_blank" >10.1007/s11042-021-10753-y</a>

Result language

angličtina

Original language name

Transfer learning with fine tuning for human action recognition from still images

Original language description

Still image-based human action recognition (HAR) is one of the most challenging research problems in the field of computer vision. Some of the significant reasons to support this claim are the availability of few datasets as well as fewer images per action class and the existence of many confusing classes in the datasets and comparing with video-based data. There is the unavailability of temporal information. In this work, we train some of the most reputed Convolutional Neural Network (CNN) based architectures using transfer learning after fine-tuned those suitably to develop a model for still image-based HAR. Since the number of images per action classes is found to be significantly less in number, we have also applied some well-known data augmentation techniques to increase the amount of data, which is always a need for deep learning-based models. Two benchmark datasets used for validating our model are Stanford 40 and PPMI, which are better known for their confusing action classes and the presence of occluded images and random poses of subjects. Results obtained by our model on these datasets outperform some of the benchmark results reported in the literature by a considerable margin. Class imbalance is deliberately introduced in the said datasets to better explore the robustness of the proposed model. The source code of the present work is available at: https://github.com/saikat021/Transfer-Learning-Based-HAR

Czech name

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Czech description

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Type

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

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Name of the periodical

Multimedia Tools and Applications

ISSN

1380-7501

e-ISSN

—

Volume of the periodical

80

Issue of the periodical within the volume

13

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

32

Pages from-to

20547-20578

UT code for WoS article

000626425600001

EID of the result in the Scopus database

2-s2.0-85102308234