Logo Detection and Identification in System for Audio-Visual Broadcast Transcription
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F21%3A00009295" target="_blank" >RIV/46747885:24220/21:00009295 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9522636" target="_blank" >https://ieeexplore.ieee.org/document/9522636</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP52935.2021.9522636" target="_blank" >10.1109/TSP52935.2021.9522636</a>
Alternative languages
Result language
angličtina
Original language name
Logo Detection and Identification in System for Audio-Visual Broadcast Transcription
Original language description
We present logo detection and identification based on a single-stage deep convolutional detector, the Scaled YOLOv4. This method is used in our system for audio-visual broadcast transcription and indexing which can be employed mainly for transcription of TV programs, mostly sports and advertising blocks. All transcribed information from audio and video streams together with time boundaries is indexed in the ElasticSearch database which can then be used to search for interesting keywords, entities etc. In this paper we present mainly the development and evaluation of the method for detection and identification of logos from images. We evaluate the logo detector on several of the most popular logo detection benchmarks, namely FlickrLogos-32, Logos-32plus, TopLogo-10 and QMUL-OpenLogo. The detector significantly outperforms the most common approach based on two stage models such as Faster R-CNN in terms of both speed and accuracy, achieving relative improvement up to 46% while running up to 2x faster.
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/TH03010018" target="_blank" >TH03010018: DeepSpot - Multilingual technology for spotting and instant alerting</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
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
44th International Conference on Telecommunications and Signal Processing
ISBN
978-166542933-7
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
357-360
Publisher name
IEEE
Place of publication
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Event location
on-line, Brno
Event date
Jan 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000701604600076