Abstract
Action recognition refers to the process of categorizing a video by identifying and classifying the specific actions it encompasses. Videos originate from several domains, and within each domain of video analysis, comprehending actions holds paramount significance. The primary aim of this research is to assist scholars in understanding, comparing, and using action recognition models within the several fields of video analysis. This paper provides a comprehensive analysis of action recognition models, comparing their performance and computational requirements. Additionally, it presents a detailed overview of benchmark datasets, which can aid in selecting the most suitable action recognition model. This review additionally examines the diverse applications of action recognition, the datasets available, the research that has been undertaken, potential future prospects, and the challenges encountered.
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