Research Article
Exploring CNN-Based Algorithms for Human Action Recognition in Videos
@INPROCEEDINGS{10.1007/978-3-031-81171-5_11, author={Shaik Salma Begum and Jami Anjana Adi Sathvik and Mohammed Ezaz Ahmed and Dantu Vyshnavi Satya and Tulasi Javvadi and Majji Naveen Sai Kuma and Kommoju V. V. S. M. Manoj Kumar}, title={Exploring CNN-Based Algorithms for Human Action Recognition in Videos}, proceedings={Broadband Communications, Networks, and Systems. 14th EAI International Conference, BROADNETS 2024, Hyderabad, India, February 16--17, 2024, Proceedings, Part II}, proceedings_a={BROADNETS PART 2}, year={2025}, month={2}, keywords={Component formatting style styling insert (keywords)}, doi={10.1007/978-3-031-81171-5_11} }- Shaik Salma Begum
Jami Anjana Adi Sathvik
Mohammed Ezaz Ahmed
Dantu Vyshnavi Satya
Tulasi Javvadi
Majji Naveen Sai Kuma
Kommoju V. V. S. M. Manoj Kumar
Year: 2025
Exploring CNN-Based Algorithms for Human Action Recognition in Videos
BROADNETS PART 2
Springer
DOI: 10.1007/978-3-031-81171-5_11
Abstract
This study presents a comparative analysis of three convolutional neural network (CNN)-based methodologies, namely the Two-Stream CNN, CNN + LSTM, and 3D CNN, for human action recognition in video sequences. The main goal of this research is to analyze and understand human behaviors in video content. And subsequently, generate associated tags, all while surmounting the intricate spatial and temporal intricacies inherent in this task. The experimental evaluation employs the HMDB-51 dataset, and the findings reveal that all three proposed algorithms effectively discern human actions within the video domain, albeit with distinct performance variations. Furthermore, the paper offers in-depth elucidations and comprehensive analyses of each of these methods, thereby imparting valuable insights and directions for prospective research endeavors in the realm of human action recognition.
