Comparative Analysis of Action Recognition Techniques: Exploring Two-Stream CNNs, C3D, LSTM, I3D, Attention Mechanisms, and Hybrid Models
Year: 2025
Type: conference-paper
Abstract: Action recognition actions in video are sophisticated processes that demand more and more explicitly captured spatial and temporal information. This paper gives a comparison of several advanced techniques for action recognition using the UCF101 dataset. We look at two-stream convolutional networks, 3D convolutional networks, long short-term memory networks, two-stream inflated 3D convolutional networks, attention mechanisms, and hybrid models. Their methods have been examined for each of the proposed options along with their architectures, as well as their pros and cons. The results of our experiments have revealed the performance of these approaches on the UCF101 dataset, including a focus on the tradeoffs between computational efficiency, data requirements, and recognition accuracy.
Authors: Arshiya, Gursharan Singh, Arun Malik, Nugraha
Institutions: Lovely Professional University, Nusa Putra University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4413889292