Human intention recognition using context relationships in complex scenes
Year: 2024
Type: article
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.
Source: Expert Systems with Applications
Authors: Tong Tong, Rossitza Setchi, Yulia Hicks
Institution: Cardiff University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,320
OpenAlex ID: W4405310279