Integrating Deep Learning Architectures for Enhanced Human Action Recognition: An Ensemble Approach
Deep Ujjwal, Kumar Sushant, Singla Kanika · International Journal of Performability Engineering · 2024
Recent developments in deep learning have revolutionized the field of activity recognition by humans and other entities.These advancements allow models to learn complex representations and hierarchies from raw data, hence increasing recognition accuracy.Many machine learning algorithms, such as support vector machines and histogram of gradients with k-nearest neighbor classifiers, have lost part of their appeal due to the extensive feature engineering and data preparation required.The objective of this research is to construct an efficient and robust ensemble model by utilizing the strengths of Convolutional neural networks (CNN), Visual Geometry Group (VGG16), Inception model, and Residual Networks 50 (ResNet50) model in order to boost the resilience and predicted accuracy of human activity recognition from raw visual data.This work makes use of a large and diverse dataset that includes over 12,000 annotated photos illustrating various human activities and methodology yields promising results while removing the need for sophisticated feature manipulation.Furthermore, the results show how the ensemble model is better at Human Action Recognition (HAR).