基于门控时空注意力的视频帧预测模型OACSTPCD

A video frame prediction model based on gated spatio-temporal attention was proposed to address the is-sues of low accuracy,slow training,complex structure,and error accumulation in recurrent video frame prediction architectures.Firstly,high-level semantic information of the video frame sequence was extracted by a spatial en-coder while preserving background features.Secondly,a gated spatio-temporal attention mechanism was estab-lished,utilizing multi-scale deep bar convolutions and channel attention to learn both intra-frame and inter-frame spatio-temporal features.A gate fusion mechanism was employed to balance the feature learning capability of spatio-temporal attention.Finally,a spatial decoder reconstructed the high-level features into predicted realistic images and complements background semantics to enhance the details.Experimental results on the Moving MNIST,Taxi-BJ,WeatherBench,and KITTI datasets showed that compared to the multi-input multi-output model SimVP,the mean squared error(MSE)was reduced by 14.7％,6.7％,10.5％,and 18.5％,respectively.In ablation and expansion experiments,the proposed model achieved good overall performance,demonstrating advantages such as high prediction accuracy,low computational complexity,and efficient inference.