Automated Actor Recognition in Video Content

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21340/25:00390919

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Automated Actor Recognition in Video Content

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Automated Actor Recognition in Video Content

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

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/GA24-10069S" target="_blank" >GA24-10069S: Hybridní architektury neuronových sítí pro rozpoznávání obrazu</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Data Science in Applications : Towards AI-Driven Approaches

ISBN

978-3-031-88485-6

Počet stran výsledku

20

Strana od-do

3-22

Počet stran knihy

302

Název nakladatele

Springer

Místo vydání

Cham

Kód UT WoS kapitoly

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