A Robust Deep Model for Human Action Recognition in Restricted Video Sequences

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A Robust Deep Model for Human Action Recognition in Restricted Video Sequences

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A Robust Deep Model for Human Action Recognition in Restricted Video Sequences

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/EF16_027%2F0008463" target="_blank" >EF16_027/0008463: Věda bez hranic</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

2020 43rd International Conference on Telecommunications and Signal Processing, TSP 2020

ISBN

978-1-72816-376-5

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

541-544

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Milán

Datum konání akce

7. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000577106400116