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Infrared Action Recognition Model Based on Improved ST-GCN.

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Title: Infrared Action Recognition Model Based on Improved ST-GCN.
Authors: Xiaoliang Zhu1 1097339974@qq.com, Ziwei Zhou2 381431970@qq.com
Source: IAENG International Journal of Computer Science. Jun2025, Vol. 52 Issue 6, p1664-1671. 8p.
Subjects: Infrared imaging, Infrared technology, Heat radiation & absorption, Structural optimization, Human mechanics
Abstract: Infrared imaging technology is capable of capturing the thermal radiation emitted by the human body in conditions with insufficient visible light. Consequently, infrared behavior recognition leverages this capability to detect and analyze human movements in low-light or complex environments. However, infrared images are often affected by noise interference, which can obscure target features. To tackle these challenges, we propose an infrared human behavior recognition model. Within this model, human regions in infrared images are detected by YOLOv8 and passed on to AlphaPose to predict the locations of skeletal keypoints in the human body. Subsequently, the acquired skeletal sequences are employed to predict actions in ST-GCN. Simultaneously, we introduced the LKA attention mechanism and the PReLU activation function for structural optimization within the ST-GCN. These improvements enabled the ST-GCN to extract action features from skeletal keypoints more effectively, thereby enhancing the accuracy of infrared behavior recognition. Through extensive ablation studies, we have demonstrated that our proposed LPST-GCN model significantly enhances the performance of infrared action recognition and achieves excellent results on both the UNISV dataset (99.02%) and the NTU RGB+D dataset (95.86%). [ABSTRACT FROM AUTHOR]
Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Infrared Action Recognition Model Based on Improved ST-GCN.
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  Data: <searchLink fieldCode="AR" term="%22Xiaoliang+Zhu%22">Xiaoliang Zhu</searchLink><relatesTo>1</relatesTo><i> 1097339974@qq.com</i><br /><searchLink fieldCode="AR" term="%22Ziwei+Zhou%22">Ziwei Zhou</searchLink><relatesTo>2</relatesTo><i> 381431970@qq.com</i>
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  Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Jun2025, Vol. 52 Issue 6, p1664-1671. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Infrared+imaging%22">Infrared imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Infrared+technology%22">Infrared technology</searchLink><br /><searchLink fieldCode="DE" term="%22Heat+radiation+%26+absorption%22">Heat radiation & absorption</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+optimization%22">Structural optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Human+mechanics%22">Human mechanics</searchLink>
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  Data: Infrared imaging technology is capable of capturing the thermal radiation emitted by the human body in conditions with insufficient visible light. Consequently, infrared behavior recognition leverages this capability to detect and analyze human movements in low-light or complex environments. However, infrared images are often affected by noise interference, which can obscure target features. To tackle these challenges, we propose an infrared human behavior recognition model. Within this model, human regions in infrared images are detected by YOLOv8 and passed on to AlphaPose to predict the locations of skeletal keypoints in the human body. Subsequently, the acquired skeletal sequences are employed to predict actions in ST-GCN. Simultaneously, we introduced the LKA attention mechanism and the PReLU activation function for structural optimization within the ST-GCN. These improvements enabled the ST-GCN to extract action features from skeletal keypoints more effectively, thereby enhancing the accuracy of infrared behavior recognition. Through extensive ablation studies, we have demonstrated that our proposed LPST-GCN model significantly enhances the performance of infrared action recognition and achieves excellent results on both the UNISV dataset (99.02%) and the NTU RGB+D dataset (95.86%). [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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        PageCount: 8
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      – SubjectFull: Infrared imaging
        Type: general
      – SubjectFull: Infrared technology
        Type: general
      – SubjectFull: Heat radiation & absorption
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      – SubjectFull: Structural optimization
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      – SubjectFull: Human mechanics
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              Text: Jun2025
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              Y: 2025
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