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arXiv · 2509.09067

Improvement of Human-Object Interaction Action Recognition Using Scene Information and Multi-Task Learning Approach

Hesham M. Shehata·Mohammad Abdolrahmani
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Abstract

Recent graph convolutional neural networks (GCNs) have shown high performance in the field of human action recognition by using human skeleton poses. However, it fails to detect human-object interaction cases successfully due to the lack of effective representation of the scene information and appropriate learning architectures. In this context, we propose a methodology to utilize human action recognition performance by considering fixed object information in the environment and following a multi-task learning approach. In order to evaluate the proposed method, we collected real data from public environments and prepared our data set, which includes interaction classes of hands-on fixed objects (e.g., ATM ticketing machines, check-in/out machines, etc.) and non-interaction classes of walking and standing. The multi-task learning approach, along with interaction area information, succeeds in recognizing the studied interaction and non-interaction actions with an accuracy of 99.25%, outperforming the accuracy of the base model using only human skeleton poses by 2.75%.

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Hesham M. Shehata, Mohammad Abdolrahmani. 2025-09-16. Improvement of Human-Object Interaction Action Recognition Using Scene Information and Multi-Task Learning Approach. https://arxiv.org/abs/2509.09067

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