Human Activity Recognition Using Weighted Average Ensemble by Selected Deep Learning Models

Human Activity Recognition (HAR) is a novel area for computer vision. It has a great impact on healthcare, smart environments, and surveillance while is able to automatically detect human behavior. It plays a vital role in many applications, such as smart home, healthcare, human computer interaction, sports analysis, and especially, intelligent surveillance. In this paper, we propose a robust and efficient HAR system by leveraging deep learning paradigms, including pre-trained models, CNN architectures, and their average-weighted fusion. However, due to the diversity of human actions and various environmental influences, as well as a lack of data and resources, achieving high recognition accuracy remain elusive. In this work, a weighted average ensemble technique is employed to fuse three deep learning models: EfficientNet, ResNet50, and a custom CNN. The results of this study indicate that using a weighted average ensemble strategy for developing more effective HAR models may be a promising idea for detection and classification of human activities. Experiments by using the benchmark dataset proved that the proposed weighted ensemble approach outperformed existing approaches in terms of accuracy and other key performance measures. The combined average-weighted ensemble of pre-trained and CNN models obtained an accuracy of 98%, compared to 97%, 96%, and 95% for the customized CNN, EfficientNet, and ResNet50 models, respectively.