Training and testing an artificial intelligence model for action recognition using the MMAction2 toolkit

Abstract

In recent decades, artificial intelligence (AI) has become an indispensable tool in various fields of human activity, including action recognition. With the advancements in machine learning and deep learning technologies, AI's capabilities in analyzing and interpreting actions performed by humans or objects in video and audio recordings have significantly increased. This progress has led to the development of numerous applications such as surveillance systems, human-computer interaction, sports analytics, and autonomous driving, where understanding and recognizing actions is crucial. Traditional methods of action recognition relied heavily on handcrafted features and classical machine learning algorithms, which often struggled with the variability and complexity of real-world scenarios. The emergence of deep learning, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), has revolutionized this field by enabling models to learn hierarchical representations directly from raw data, thus improving performance and robustness. In this paper, we describe the process of training and testing an artificial intelligence model in an action recognition task using the MMAction2 toolkit.