Basketball Motion Recognition Model Analysis Based on Perspective Invariant Geometric Features in Skeleton Data Extraction

The study proposes a recognition method based on skeleton data to address the basketball action recognition, especially those posed by viewpoint changes in videos. The key of this method is to extract geometric features of viewpoint invariance and combine them with spatio-temporal feature fusion techniques. In addition, the study constructs a dynamic topological map of the human skeleton based on long and short-term neural networks to improve the model performance. The experimental results showed that the research method had an average accuracy of 97.85% for Top-5 metrics on the Kinetics dataset and 97.82% for Top-5 metrics on the NTU RGB+D dataset. It is significantly better than the other three state-of-the-art methods. According to the experimental results, it achieves efficient and stable basketball action recognition, which is significantly superior to existing methods. This research not only provides a more efficient method for basketball motion recognition, but also provides valuable references for other sports action recognition fields.

@article{Lu2023,
  title     = {Basketball Motion Recognition Model Analysis Based on Perspective Invariant Geometric Features in Skeleton Data Extraction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Jiaojiao Lu},
  doi       = {10.14569/IJACSA.2023.0141150},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141150}
}