Thermal infrared action recognition with two-stream shift Graph Convolutional Network

The extensive deployment of camera-based IoT devices in our society is heightening the vulnerability of citizens’ sensitive information and individual data privacy. In this context, thermal imaging techniques become essential for data desensitization, entailing the elimination of sensitive data to safeguard individual privacy. Meanwhile, thermal imaging techniques can also play a important role in industry by considering the industrial environment with low resolution, high noise and unclear objects’ features. Moreover, existing works often process the entire video as a single entity, which results in suboptimal robustness by overlooking individual actions occurring at different times. In this paper, we propose a lightweight algorithm for action recognition in thermal infrared videos using human skeletons to address this. Our approach includes YOLOv7-tiny for target detection, Alphapose for pose estimation, dynamic skeleton modeling, and Graph Convolutional Networks (GCN) for spatial-temporal feature extraction in action prediction. To overcome detection and pose challenges, we created OQ35-human and OQ35-keypoint datasets for training. Besides, the proposed model enhances robustness by using visible spectrum data for GCN training. Furthermore, we introduce the two-stream shift Graph Convolutional Network to improve the action recognition accuracy. Our experimental results on the custom thermal infrared action dataset (InfAR-skeleton) demonstrate Top-1 accuracy of 88.06% and Top-5 accuracy of 98.28%. On the filtered kinetics-skeleton dataset, the algorithm achieves Top-1 accuracy of 55.26% and Top-5 accuracy of 83.98%. Thermal Infrared Action Recognition ensures the protection of individual privacy while meeting the requirements of action recognition.