@Article{cmes.2025.068941,
AUTHOR = {Lakshmi Alekhya Jandhyam, Ragupathy Rengaswamy, Narayana Satyala},
TITLE = {Active Learning-Enhanced Deep Ensemble Framework for Human Activity Recognition Using Spatio-Textural Features},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {144},
YEAR = {2025},
NUMBER = {3},
PAGES = {3679--3714},
URL = {http://www.techscience.com/CMES/v144n3/63952},
ISSN = {1526-1506},
ABSTRACT = {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.},
DOI = {10.32604/cmes.2025.068941}
}