0638575 20260226075133.120250903235959.9 105015556978 10.1007/978-3-031-88486-3_1 20 s. 302 P cav_un_epca*0638914978-3-031-88485-63-22ChamSpringer2025DzemydaG.BernatavičienėJ.KacprzykJ. Actor recognition Face detection Video analysis Screen time calculation cav_un_auth*0379363 Kerepecký Tomáš UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101209 Šroubek Filip UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101238 Zitová Barbara UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101087 Flusser Jan UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2025/ZOI/kerepecky-0638575.pdf^[object Object]^[object Object] GA24-10069S GA ČR CZ cav_un_auth*0472834 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. IN 10000 10200 10201 2026 4 RVO:67985556 https://hdl.handle.net/11104/0369467 S 2025 105015556978 SCOPUS PUBMED WOS cav_un_epca*0638914 Data Science in Applications : Towards AI-Driven Approaches Springer 2025 Cham 3 22 978-3-031-88485-6 Studies in Computational Intelligence Dzemyda G. 340 Bernatavičienė J. 340 Kacprzyk J. 340