  Advancements in egocentric video datasets like Ego4D, EPIC-Kitchens, and
Ego-Exo4D have enriched the study of first-person human interactions, which is
crucial for applications in augmented reality and assisted living. Despite
these advancements, current Online Action Detection methods, which efficiently
detect actions in streaming videos, are predominantly designed for exocentric
views and thus fail to capitalize on the unique perspectives inherent to
egocentric videos. To address this gap, we introduce an Object-Aware Module
that integrates egocentric-specific priors into existing OAD frameworks,
enhancing first-person footage interpretation. Utilizing object-specific
details and temporal dynamics, our module improves scene understanding in
detecting actions. Validated extensively on the Epic-Kitchens 100 dataset, our
work can be seamlessly integrated into existing models with minimal overhead
and bring consistent performance enhancements, marking an important step
forward in adapting action detection systems to egocentric video analysis.
