Deep Learning Approaches for Human Action Recognition in Video Data

TL;DR

This paper analyzes deep learning models like CNNs, RNNs, and Two-Stream ConvNets for human action recognition in videos, highlighting the superior performance of Two-Stream ConvNets in integrating spatial and temporal features.

Contribution

It provides a comparative analysis of deep learning architectures for action recognition and demonstrates the effectiveness of Two-Stream ConvNets over other models.

Findings

Two-Stream ConvNets outperform CNNs and RNNs in accuracy and robustness.

Deep learning models effectively capture spatial and temporal features for action recognition.

Evaluation metrics confirm the potential of composite models for real-world applications.

Abstract

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.