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Part Affinity Field based Activity Recognition

Golda, Thomas


This report presents work and results on Activity Recognition using Part Affinity Fields for real-time surveillance applications. Starting with a short introduction to the motivation, this report gives a detailed overview over the key idea of the pursued approach and explains the basic ideas. In addition a variety of experiments on various subjects are presented, like i) the impact of the number of input frames, ii) the impact of different simple dimensionality reduction approaches, and iii) a comparison on how multi-class and binary problem formulation influence the performance.

Verlagsausgabe §
DOI: 10.5445/IR/1000126337
Veröffentlicht am 30.11.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 14.10.2020
Sprache Englisch
Identifikator ISBN: 978-3-7315-1028-4
ISSN: 1863-6489
KITopen-ID: 1000126337
Erschienen in Proceedings of the 2019 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory / by Jürgen Beyerer, Tim Zander (eds.)
Verlag KIT Scientific Publishing
Seiten 53-67
Serie Karlsruher Schriften zur Anthropomatik ; 45
Bemerkung zur Veröffentlichung Technical Report IES-2019-1
Schlagwörter action recognition, activity recognition, machine learning, video surveillance,
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