Video scene location recognition with neural networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10450952" target="_blank" >RIV/00216208:11320/21:10450952 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/21:00351646
Result on the web
<a href="https://ceur-ws.org/Vol-2962/paper37.pdf" target="_blank" >https://ceur-ws.org/Vol-2962/paper37.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Video scene location recognition with neural networks
Original language description
This paper provides an insight into the possibility of scene recognition from a video sequence with a small set of repeated shooting locations (such as in television series) using artificial neural networks. The basic idea of the presented approach is to select a set of frames from each scene, transform them by a pre-trained single-image pre-processing convolutional network, and classify the scene location with subsequent layers of the neural network. The considered networks have been tested and compared on a dataset obtained from The Big Bang Theory television series. We have investigated different neural network layers to combine individual frames, particularly AveragePooling, MaxPooling, Product, Flatten, LSTM, and Bidirectional LSTM layers. We have observed that only some of the approaches are suitable for the task at hand.
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/LM2018131" target="_blank" >LM2018131: Czech National Infrastructure for Biological Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
CEUR Workshop Proceedings
ISBN
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ISSN
1613-0073
e-ISSN
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Number of pages
9
Pages from-to
85-93
Publisher name
CEUR-WS
Place of publication
Neuveden
Event location
Muránska planina, Slovakia
Event date
Sep 24, 2021
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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