Few-Shot Sports Posture Recognition Using TGAN-LSTM with Transfer Learning and GAN

Many existing techniques for recognizing sports posture depend heavily on large, labeled datasets, which limits their performance in scenarios with limited data, such as few-shot learning in specific sports. To address this challenge, we introduce a novel framework that integrates transfer learning, LSTM (Long Short-Term Memory), and GAN (Generative Adversarial Network). The transfer learning component extracts common features from large-scale data in the source domain, while the LSTM module captures temporal dependencies crucial for posture recognition. Simultaneously, the GAN component generates synthetic data to supplement the sparse dataset in the target domain, improving the overall model effectiveness. Experimental results on the Human3.6M and Kinetics-400 datasets show that the proposed TGAN-LSTM model outperforms others in key evaluation metrics, achieving an accuracy of 85.2%, mAP of 79.8%, and AUC of 87.3% on Human3.6M, and an accuracy of 80.7% with an F1-score of 78.5% on Kinetics-400. In comparison to other baseline models, TGANLSTM demonstrates remarkable performance under few-shot learning conditions. This approach not only offers a solution for few-shot sports posture recognition but also contributes valuable insights to research in cross-domain transfer learning and few-shot learning, with wide practical applications.