Active Learning-Enhanced Deep Ensemble Framework for Human Activity Recognition Using Spatio-Textural Features

Human Activity Recognition (HAR) has become increasingly critical in civic surveillance, medical care monitoring, and institutional protection. Current deep learning-based approaches often suffer from excessive computational complexity, limited generalizability under varying conditions, and compromised real-time performance. To counter these, this paper introduces an Active Learning-aided Heuristic Deep Spatio-Textural Ensemble Learning (ALH-DSEL) framework. The model initially identifies keyframes from the surveillance videos with a Multi-Constraint Active Learning (MCAL) approach, with features extracted from DenseNet121. The frames are then segmented employing an optimized Fuzzy C-Means clustering algorithm with Firefly to identify areas of interest. A deep ensemble feature extractor, comprising DenseNet121, EfficientNet-B7, MobileNet, and GLCM, extracts varied spatial and textural features. Fused characteristics are enhanced through PCA and Min-Max normalization and discriminated by a maximum voting ensemble of RF, AdaBoost, and XGBoost. The experimental results show that ALH-DSEL provides higher accuracy, precision, recall, and F1-score, validating its superiority for real-time HAR in surveillance scenarios.