  Human action recognition in videos is a critical task with significant
implications for numerous applications, including surveillance, sports
analytics, and healthcare. The challenge lies in creating models that are both
precise in their recognition capabilities and efficient enough for practical
use. This study conducts an in-depth analysis of various deep learning models
to address this challenge. Utilizing a subset of the UCF101 Videos dataset, we
focus on Convolutional Neural Networks (CNNs), Recurrent Neural Networks
(RNNs), and Two-Stream ConvNets. The research reveals that while CNNs
effectively capture spatial features and RNNs encode temporal sequences,
Two-Stream ConvNets exhibit superior performance by integrating spatial and
temporal dimensions. These insights are distilled from the evaluation metrics
of accuracy, precision, recall, and F1-score. The results of this study
underscore the potential of composite models in achieving robust human action
recognition and suggest avenues for future research in optimizing these models
for real-world deployment.
