Optimizing human action classification: Data preprocessing and feature-based splitting strategies for enhanced model performance

The human action classification (HAC) problem presents a complex challenge in the field of machine learning, particularly when dealing with random and unpredictable activities. Resource-intensive models often require significant computational power, limiting their deployment on devices with constrained resources. To address this, we propose a method to improve the accuracy of action classification models by focusing on data preprocessing and data set splitting based on feature clustering using K-means, rather than relying solely on complex models. While we employ ResNet50 and EfficientNetV2 for their proven effectiveness in handling complex features, we also utilize MobileNetV3 as an efficient alternative suitable for resource-constrained scenarios. The experimental results demonstrate that the proposed method significantly improves accuracy and model generalization using the HMDB51 data set. This feature-based data-splitting approach effectively reduces overfitting and can be integrated with other techniques to further optimize model training efficiency.