To address the issues of low accuracy in existing 3D human pose estimation (HPE) methods and the limited level of details in Labanotation, we propose an extended Labanotation generation method for intangible cultural heritage dance videos based on 3D HPE. First, a 2D human pose sequence of the performer is inputted along with spatial location embeddings, where multiple spatial transformer modules are employed to extract spatial features of human joints and generate cross-joint multiple hypotheses. Afterward, temporal features are extracted by a self-attentive module and the correlation between different hypotheses is learned using bilinear pooling. Finally, the 3D joint coordinates of the performer are predicted, which are matched with the corresponding extended Labanotation symbols using the Laban template matching method to generate extended Labanotation. Experimental results show that, compared with VideoPose and CrossFormer algorithms, the Mean Per Joint Position Error (MPJPE) of the proposed method is reduced by 3.7[Formula: see text]mm and 0.6[Formula: see text]mm, respectively on Human3.6M dataset, and the generated extended Labanotation can better describe the movement details compared with the basic Labanotation.

Social distance monitoring is of great significance for public health in the era of COVID-19 pandemic. However, existing monitoring methods cannot effectively detect social distance in terms of efficiency, accuracy, and robustness. In this paper, we proposed a social distance monitoring method based on an improved YOLOv4 algorithm. Specifically, our method constructs and pre-processes a dataset. Afterwards, our method screens the valid samples and improves the K-means clustering algorithm based on the IoU distance. Then, our method detects the target pedestrians using a trained improved YOLOv4 algorithm and gets the pedestrian target detection frame location information. Finally, our method defines the observation depth parameters, generates the 3D feature space, and clusters the offending aggregation groups based on the L2 parametric distance to finally realize the pedestrian social distance monitoring of 2D video. Experiments show that the proposed social distance monitoring method based on improved YOLOv4 can accurately detect pedestrian target locations in video images, where the pre-processing operation and improved K-means algorithm can improve the pedestrian target detection accuracy. Our method can cluster the offending groups without going through calibration mapping transformation to realize the pedestrian social distance monitoring of 2D videos.

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