A Robust Deep Model for Human Action Recognition in Restricted Video Sequences
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246689" target="_blank" >RIV/61989100:27240/20:10246689 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9163464" target="_blank" >https://ieeexplore.ieee.org/document/9163464</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP49548.2020.9163464" target="_blank" >10.1109/TSP49548.2020.9163464</a>
Alternative languages
Result language
angličtina
Original language name
A Robust Deep Model for Human Action Recognition in Restricted Video Sequences
Original language description
In this paper, we propose an action recognition algorithm in noisy data conditions with Convolutional Neural Network (CNN) as the front end and Deep Bidirectional Long Short Term Memory (DBi-LSTM) as the backend. The deep features are extracted from the input frames using a VGG16 model. The sequential information among frames is learned using the DBi-LSTM part, which is composed of three layers stacked together in both forward and backward directions to increase the learning depth. The proposed algorithm achieved 96.77% vs. 96.76% and 95.83% vs. 91.60% accuracy of the baseline methods on KTH and YouTube datasets, respectively. Moreover, the proposed algorithm has shown significant robustness in noisy training data as the accuracy drops only 1% down. (C) 2020 IEEE.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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)
Result continuities
Project
<a href="/en/project/EF16_027%2F0008463" target="_blank" >EF16_027/0008463: Science without borders</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 43rd International Conference on Telecommunications and Signal Processing, TSP 2020
ISBN
978-1-72816-376-5
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
541-544
Publisher name
IEEE
Place of publication
Piscataway
Event location
Milán
Event date
Jul 7, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000577106400116