Revolutionizing Human Action Prediction and Detection through Extended Transfer Learning: A Novel Approach for Cross-Domain Knowledge Integration

The study seeks to propel the domain of human action prediction and detection forward through the introduction of a novel methodology, utilizing extended transfer learning to seamlessly incorporate cross-domain knowledge, ultimately improving the performance and versatility of the models. The Human Action Dataset, as detailed, comprises 15 action classes, with a specific study focusing on a subset of 3 classes. Each of these classes is represented by a balanced set of 1000 training images and 200 testing images, facilitating a thorough examination of human action detection models within a standardized dataset structure. Effectively predicting human actions across diverse datasets requires skillful management of domain shift and robust generalization in the transfer learning model, while concurrently addressing the challenge of determining optimal architecture and hyperparameter settings for extended transfer learning, involving careful considerations of model complexity, layer adaptations, and hyperparameter tuning to optimize performance across varied domains. To address the issues related to domain shift and model refinement, utilize two advanced transfer learning methods namely integrate domain-adaptive transfer learning to improve generalization and apply architecture-specific fine-tuning for optimal model performance in predicting human actions across varied datasets. The suggested extended Transfer Learning method enhances its adaptability to varied datasets, improving the performance of human action prediction, and concurrently diminishing the requirement for extensive labeled data by leveraging existing knowledge from the source domain. The test accuracy, standing at 86.5%, further confirms the model’s proficiency in predicting human actions.