Masked Autoencoders for Spatial-Temporal Relationship in Video-Based Group Activity Recognition

Group Activity Recognition (GAR) is a challenging problem involving several intricacies. The core of GAR lies in delving into spatiotemporal features to generate appropriate scene representations. Previous methods, however, either feature a complex framework requiring individual action labels or nee...

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Vydané v:	IEEE access Ročník 12; s. 132084 - 132095
Hlavní autori:	Yadav, Rajeshwar, Halder, Raju, Banda, Gourinath
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Activity recognition CAD Cognitive tasks Collaborative software Complexity Computer aided design Crime Datasets Encoding Feature recognition Group activity recognition (GAR) hostage crime IITP hostage dataset Image reconstruction Labels masked autoencoder Predictive models Representations Solid modeling spatial and temporal interaction Spatiotemporal data Spatiotemporal phenomena Video Videos vision transformer
ISSN:	2169-3536, 2169-3536
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