Deep Learning Approaches for Human Action Recognition in Video Data
It addresses the need for precise and efficient action recognition in applications like surveillance and healthcare, but is incremental as it compares existing methods without introducing new techniques.
This study analyzed deep learning models for human action recognition in videos, finding that Two-Stream ConvNets outperformed CNNs and RNNs by integrating spatial and temporal features, as measured by accuracy, precision, recall, and F1-score on the UCF101 dataset.
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.