Deep Learning-Based Behavior Analysis in Basketball Video: A Spatiotemporal Approach

The study of sports movement analysis technologies based on video has significant practical applications. Digital video footage, human-computer communication, as well as additional technologies can greatly improve the effectiveness of sports training. This research looks at the players’ technical proficiency in a basketball contest footage and suggests a behaviour assessment technique inspired by the use of deep learning and attention mechanisms. First, we develop an approach for effortlessly obtaining the marking lines from the basketball arena and stadium. After that, the most significant frames of the footage have been shot using a spatial and temporal ranking technique. Next, we design a behaviour comprehension and prediction technique by implementing an autoencoder design. The results of the study may be sent to instructors and data scientists instantly to support them in determining their strategies and professional decisions. An extensive dataset of basketball films is used to test the proposed method. The outcomes demonstrate that the recommended attention mechanism-based strategy competently recognises the movement of video individuals while attaining substantial behavioural assessment efficiency.

@article{Wang2025,
  title     = {Deep Learning-Based Behavior Analysis in Basketball Video: A Spatiotemporal Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {3},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Jingyi Wang},
  doi       = {10.14569/IJACSA.2025.01603102},
  url       = {https://doi.org/10.14569/IJACSA.2025.01603102}
}