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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • e-ISSN

  • 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