Predicting how pedestrians will move in the future is crucial for robot navigation, autonomous driving, and video surveillance. The complex interactions among pedestrians make it difficult to predict their future trajectory. Previous studies have primarily focused on modeling the interaction features of all pedestrians in the scene. However, this approach often results in an abundance of irrelevant interactions and overlooks the time-dependent characteristics. To address these issues, we propose a spatio-temporal adaptive graph pooling network (STAGP) for pedestrian trajectory prediction. STAGP adopts adaptive graph pooling to explicitly model interactions between pedestrians, redundant interactions and establishing directed interactions. In addition, we utilize spatio-temporal attention to extract temporal features of pedestrian interactions. For the prediction of future trajectories, we use a time-extrapolator convolutional neural network (TXP-CNN). ETH and UCY datasets were used to evaluate STAGP. Comparing STAGP to the values, the experimental results indicate that it is competitive in terms of ADE and FDE metrics.

Head pose estimation is an important technology for analyzing human behavior and has been widely researched and applied in areas such as human–computer interaction and fatigue detection. However, traditional head pose estimation networks suffer from the problem of easily losing spatial structure information, particularly in complex scenarios where occlusions and multiple object detections are common, resulting in low accuracy. To address the above issues, we propose a head pose estimation model based on the residual network and capsule network. Firstly, a deep residual network is used to extract features from three stages, capturing spatial structure information at different levels, and a global attention block is employed to enhance the spatial weight of feature extraction. To effectively avoid the loss of spatial structure information, the features are encoded and transmitted to the output using an improved capsule network, which is enhanced in its generalization ability through self-attention routing mechanisms. To enhance the robustness of the model, we optimize Huber loss, which is first used in head pose estimation. Finally, experiments are conducted on three popular public datasets, 300W-LP, AFLW2000, and BIWI. The results demonstrate that the proposed method achieves state-of-the-art results, particularly in scenarios with occlusions.