Abstract
Human activity recognition, as a significant branch of artificial intelligence, requires increasingly generalized and precise methodologies due to growing demands. Therefore, this paper proposes a context-based method for recognising indoor human activities, which interlinks indoor human activities with interactions with objects, making the contextual relationship between humans and objects particularly crucial. In addition, this research has developed a new dynamic graph dataset based on publicly available video datasets and their associated descriptive scripts, instantiating the relationships between humans and objects. A novel architecture for human activity recognition is developed in this research. This architecture utilizes graph neural networks and self-attention mechanisms to learn the significance of the interactions between humans and objects and capture the relationships between video frames on a temporal level. The results demonstrate that the classification accuracy reaches 0.86 and it also performs better than other current advanced algorithms STGAT and STGCN. It is noteworthy that the approach also effectively reduces ambiguity in activity recognition.
Published Version
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