Video Analysis and Frame Prediction Based on Improved Object Detection and ConvGRU

Video analysis is of crucial importance in areas such as information dissemination, understanding, and environmental perception. To further improve video processing technology and increase video fluency, this article innovatively uses improved object detection algorithms for video analysis. The study also improves neural networks to construct video frame prediction models with the help of motion perception, synthetic streaming, and other technologies. The results demonstrated that the improved algorithm has significantly improved its detection performance after introducing improvement measures, with a detection accuracy of 0.988, a floating-point calculation count of 5.732G, and a detection speed of 270.646fps. The loss function and average accuracy mean have been improved, with a maximum detection accuracy of 0.947. The PSNR, structural similarity, and interpolation error of the frame prediction model were superior to existing advanced models. Comparative models included future frame prediction models based on generative auxiliary discrimination networks, multi-source prediction frameworks based on spherical convolution, and inverted pyramid prediction models based on cross-optical flow registration. In summary, the designed video analysis and frame prediction methods have achieved efficient and high-quality video processing. This study can effectively enhance the ability of video analysis and frame prediction, promote the innovation and development of video analysis technology, and accelerate the development of intelligent video applications.