Narrow Results

Human skeleton-based posture anomaly detection has been widely applied in the field of physical education teaching. The existing spatio-temporal graph convolutional networks (ST-GCN) can fully utilize the local and global information of the human skeleton for action recognition, but the entire model requires a large amount of computation and the modeling of high-order relationships between joint points of the human skeleton is insufficient. To this end, this paper proposes a novel domain adaptive hypergraph convolutional network for basketball posture anomaly analysis by exploiting 2D skeleton information. First, we designed an effective hypergraph convolution feature extraction network to improve the high-order dependency modeling. To further improve the performance of the hypergraph convolutional network, we introduce domain adaptive learning technology to supervise the feature extraction learning of the target domain (2D skeleton) through the source domain (3D skeleton). At last, we construct a basketball action teaching analysis dataset for model evaluation. We conducted a large number of comparative experiments on the public dataset NTU RGB+D and our self-built dataset. All the results showed that our proposed hypergraph convolutional model effectively extracts features of 2D human skeletons, and by introducing domain adaptive learning, the performance of basketball anomaly detection is further improved.

Deep learning-based models have achieved promising performance for video behavior anomaly detection, but these models require high computational complexity for processing video data. Moreover, the performance is severely restricted by different challenges, i.e., lighting conditions, background noise and occlusion. The human skeleton has a compact spatial structure and rich semantic information, which can be more robust to video data defection. Although the human skeleton has good real-time performance, the recognition accuracy still needs further improvement. To this end, we propose a novel discrete variational feature transfer learning (DVFTL) framework in which the spatiotemporal graph-embedded Transformer module is designed to construct feature extraction backbones. Specifically, we extract human skeleton information from video frames, and combine graph convolution with the Transformer encoder to explore the local and global dependencies of joint points in the human skeleton. To analyze abnormal behavior uncertainty, we constructed the extraction network based on the discrete variational reconstruction mechanism. Moreover, to further improve the skeleton detection performance, we introduce the distillation transfer learning mechanism from the video variational network to the skeleton variational network. we conducted extensive comparative experiments on two publicly available datasets. The experimental results show that the skeleton-based variational network achieves high performance. Moreover, the introduction of the transfer learning mechanism can further improve the performance of our proposed model.