BASKETech

BASKETech: Basketball Technique Correction with Action Recognition and Temporal Feature Extraction Overview BASKETech is an intelligent system for basketball technique correction that integrates action recognition, temporal feature extraction, and an action comparison mechanism.This framework leverages advanced multimodal deep learning, combining spatial and temporal modeling, to provide real-time, personalized feedback on player technique. The core architecture includes: DTFEM (Dynamic Temporal Feature Extraction Model) Multimodal Encoder Architecture Adaptive Feedback Mechanism Together, these modules enable high-precision recognition, real-time correction, and automated training insights, making BASKETech suitable for professional athletes, amateur players, and sports analytics research. ✨ Features Dynamic Temporal Feature Extraction Captures spatial features via convolutional layers and temporal dynamics using recurrent networks. Integrates reduced self-attention, Mamba blocks, and context broadcasting networks. Enables fine-grained recognition of dribbling, shooting, passing, and complex basketball movements.(See Fig. 1, p.7 for DTFEM architecture.) Multimodal Encoder Architecture Combines CNN and LSTM modules to model both frame-level details and long-term dependencies.(See Fig. 2, p.8 for encoder design.) Supports dynamic local adapters for vision-language fusion, improving contextual understanding. Action Comparison Mechanism Aligns extracted player features with ideal reference techniques using similarity measures. Computes similarity scores with Euclidean or cosine distance to highlight deviations. Triggers adaptive corrective feedback for real-time improvement. Adaptive Feedback Mechanism Provides immediate and personalized training suggestions. Enables player-specific performance monitoring and technique optimization over time. 📊 Datasets Dataset Description Use Basketball Player Motion Analysis Dataset High-resolution annotated basketball motion sequences Technique recognition and training Temporal Feature Extraction in Sports Dataset Time-sequenced video clips with event markers Temporal modeling Action Recognition in Basketball Dataset Offensive & defensive plays annotated with labels Model training and evaluation Comparative Basketball Technique Dataset Recordings of varied technique styles for comparison Benchmarking and feedback optimization 🚀 Usage Detected action labels Temporal feature curves Similarity scores vs. ideal technique Real-time correction feedback 🧪 Applications Real-time basketball technique correction Performance monitoring and athlete development Intelligent coaching and AR-assisted sports training Sports biomechanics research and analytics 🧩 Model Components DTFEM — Dynamic temporal feature extractor (Fig. 1, p.7). Multimodal Encoder Architecture — CNN + LSTM integration (Fig. 2–4, p.8–10). Action Recognition & Temporal Feature Strategy — vision-language transformer alignment (Fig. 3, p.9). Adaptive Feedback Mechanism — similarity-based technique correction. Graphical Propagation Layer — attention-based focus on key temporal segments. 📈 Performance Dataset Accuracy Recall F1 Score AUC Basketball Player Motion Analysis 89.67 89.12 88.45 88.89 Temporal Feature Extraction 91.12 90.56 89.89 90.34 Action Recognition in Basketball 89.67 89.12 88.45 88.89 Comparative Basketball Technique 91.12 90.56 89.89 90.23 This framework outperforms ResNet, ViT, I3D, BLIP, DenseNet, and MobileNet baselines. 🧭 Future Work Optimize model for mobile and edge deployment. Expand dataset to cover 3D skeletal pose estimation. Enhance adaptability for player-specific technique templates. Integrate with mixed reality or AR/VR training environments. 📜 License This project is licensed under the MIT License. 🙏 Acknowledgments This work was developed at School of Physical Education and Health, Zhaoqing University.Author: Zhixing Zhou.This project integrates deep learning and temporal analysis to support real-time, scalable basketball technique correction.