Infrared Action Recognition Model Based on Improved ST-GCN.

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	1819656X