@article{3820,
  author = {Lian Qian},
  title = {Experiments with Offline Visual Processing Modules for Learning Networks},
  journal = {Journal of Multimedia Processing and Technologies},
  year = {2023},
  volume = {14},
  number = {3},
  doi = {https://doi.org/10.6025/jmpt/2023/14/3/78-85},
  url = {https://www.dline.info/jmpt/fulltext/v14n3/jmptv14n3_3.pdf},
  abstract = {This article analyzes the visual processing module of table tennis using an offline learning network model. By training and learning a large amount of video data from table tennis matches, offline learning network models can automatically extract features from table tennis movements and accurately perform object detection, trajectory prediction, and action recognition. Unlike traditional visual processing methods, offline learning network models have higher accuracy and efficiency. Summarized the advantages and disadvantages of visual processing module analysis for table tennis based on an offline learning network model. Offline learning network models can automatically extract features, reduce manual intervention, and improve processing efficiency. At the same time, this model requires a large amount of training data and computing resources, resulting in high time and computational costs. In future research, offline learning network models can be further optimized to improve their processing speed and accuracy, providing more accurate and efficient visual aids for table tennis players.},
}