An analysis of Video Categorization, including its Approaches, Results, Performance, Problems, Solutions, and Future Directions
Abstract
Internet accessibility and bandwidth have improved dramatically in recent years. Since connecting to the Internet is now so cheap, it has facilitated the widespread and rapid dissemination of information in the forms of text, audio, and video. Predicting the appropriate category for this video footage is necessary for a variety of uses. For the sake of human efficiency, several machine-learning approaches have been created for video categorization. Existing review articles on video classification have a number of drawbacks, including limited analysis, poor organization, failure to disclose research gaps or conclusions, and inadequate description of benefits, drawbacks, and future directions for investigation. However, we believe that our review article comes close to surpassing these constraints. This research aims to provide a comprehensive overview of the current state of video categorization by analyzing and comparing the many approaches now in use and recommending the way that has shown to be the most successful and efficient. First, we look at how films are categorized using taxonomy, current applications, processes, and datasets. Second, the current connection in science, deep learning, and the model of machine learning, as well as the associated inconveniences, challenges, flaws, and possible work, data, and performance assessments. The study of video classification systems, including their characteristics, tools, advantages, and disadvantages, for the purpose of comparing the methods they have used, is a significant part of this review. Finally, we provide a tabular overview of key aspects. The RNN, CNN, and combination technique outperforms the CNN-dependent approach in terms of accuracy and independence extraction functions.
Keywords
Video Classification, Machine learning, Deep learning, Video, Video classificationHow to Cite
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