Automated Actor Recognition in Video Content
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00638575" target="_blank" >RIV/67985556:_____/25:00638575 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/25:00390919
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-88486-3_1" target="_blank" >http://dx.doi.org/10.1007/978-3-031-88486-3_1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-88486-3_1" target="_blank" >10.1007/978-3-031-88486-3_1</a>
Alternative languages
Result language
angličtina
Original language name
Automated Actor Recognition in Video Content
Original language description
This chapter presents an AI pipeline designed for automated recognition and analysis of actors in video content. The pipeline incorporates advanced methodologies in computer vision, allowing for a comprehensive analysis of actor presence and screen time across various video formats, such as movies, television shows, and surveillance footage. To evaluate the pipeline performance, we conducted extensive experiments using a carefully annotated test videos from a Czech TV show available for download. The evaluation criteria focus on precision, recall, mean absolute error metrics for actor recognition and screen time calculation under varying conditions. Additionally, we discuss challenges encountered during the pipeline development and consider its potential implications for the future of AI-driven content analysis and security surveillance.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
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/GA24-10069S" target="_blank" >GA24-10069S: Hybrid neural network architectures for image recognition</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Data Science in Applications : Towards AI-Driven Approaches
ISBN
978-3-031-88485-6
Number of pages of the result
20
Pages from-to
3-22
Number of pages of the book
302
Publisher name
Springer
Place of publication
Cham
UT code for WoS chapter
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