Long-term Action Anticipation: A Quick Survey
Abstract (englisch):
The ability to anticipate possible human actions in the distant future is of fundamental interest for a wide range of applications, including autonomous driving, surveillance, and human-robot interaction. Consequently, various methods have been presented for action anticipation in recent years, with deep learning-based approaches being particularly popular. In this work, we give a short overview of the recent advances of long-term action anticipationalgorithms.
| Zugehörige Institution(en) am KIT | Institut für Anthropomatik und Robotik (IAR) |
| Publikationstyp | Proceedingsbeitrag |
| Publikationsjahr | 2023 |
| Sprache | Englisch |
| Identifikator | ISBN: 978-3-7315-1304-9 ISSN: 1863-6489 KITopen-ID: 1000162010 |
| Erschienen in | Proceedings of the 2022 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. Ed.: J. Beyerer |
| Verlag | KIT Scientific Publishing |
| Seiten | 109-119 |
| Serie | Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe ; 62 |
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