Human intention recognition using context relationships in complex scenes

Tong, Tong, Setchi, Rossitza ORCID: https://orcid.org/0000-0002-7207-6544 and Hicks, Yulia ORCID: https://orcid.org/0000-0002-7179-4587 2025. Human intention recognition using context relationships in complex scenes. Expert Systems with Applications 266 , 126147. 10.1016/j.eswa.2024.126147

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Abstract

Recognizing human intentions is a key challenge in human-robot interaction research. Much of the current work in this area centers on identifying human intentions within specific activities, often relying on a limited set of features. In contrast, this paper introduces a more versatile framework for intention recognition and introduces a novel model: the Spatial-Temporal Graph Attention Informer Neural Network (STGAIN). To recognize intentions, this model leverages spatial relationships between humans and objects in different scenes, along with their temporal evolution. In addition, to address an existing research gap, this research developed a new dataset called Dynamic Scene Graph (DSG) with representative dynamic relationships, derived from 471 videos covering 20 categories of human intentions. This dataset represents people and objects in different scenes, and the relationships between them. The model was tested rigorously at different points in the videos to track how the scenes evolved and to assess prediction accuracy, comparing the results to a range of advanced algorithms. Our findings clearly demonstrate that STGAIN outperforms these models, showcasing its potential for advanced human intention recognition applications. This model represents a significant advance toward creating more human-centered robots, capable of understanding and adapting to human intentions in real-world situations.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0957-4174
Date of First Compliant Deposit:	19 December 2024
Date of Acceptance:	9 December 2024
Last Modified:	06 Jan 2025 15:34
URI:	https://orca.cardiff.ac.uk/id/eprint/174841

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