Robust Abnormal Human-Posture Recognition Using OpenPose and Multiview Cross-Information

With the emerging demand for intelligent health surveillance, video information has been widely explored to facilitate real-time automatic patient monitoring systems. Recently, human-posture recognition techniques based on deep learning or artificial intelligence networks have been reported in the literature. Nonetheless, during the training, testing, or both stages, it is quite difficult to extract reliable features for all postures to be recognized. In this work, we propose a robust multiperspective abnormal human-posture recognition approach based on the multiview cross-information and the confidence measurement, which is adopted to evaluate the importance of the posture-feature information from different perspectives. In our proposed approach, human skeletal data are first extracted by OpenPose. Then those data are utilized as the input of the YOLOv5s system to recognize/detect abnormal postures such as falls and bumps. Based on the NTU-RGB+D public dataset and the Pytorch framework, the simulation results show that our proposed abnormal human-posture recognition method can lead to high accuracy.