Domenico Mereu
MARE-Graph: Multimodal Action Recognition in Egocentric video with Graph Neural Network.
Rel. Giuseppe Bruno Averta, Simone Alberto Peirone, Francesca Pistilli, Antonio Alliegro. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2024
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Abstract
In recent years, the growth of affordable wearable cameras, exemplified by devices like GoPro, has yielded a growing interest in first-person perspective, denoted as egocentric vision. The proximity of the camera to actions allows for a deep analysis of human behavior and human-environment interaction. The benefit of egocentric vision finds exploitation in numerous applications, including augmented and mixed reality, human-robot interaction and behavior understanding. Tasks related to video analysis demand a focus on the integration of diverse modalities due to their inherently multimodal nature. The inclusion of additional modalities provides complementary information, addressing limitations and enhancing the robustness and accuracy of egocentric action recognition systems.
Nevertheless, the integration of diverse modalities introduces challenges arising from data heterogeneity, distinct preprocessing needs, and varying computational demands specific to each modality
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