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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Md Shofiqul Islam
Faculty of Soft computing, FSKKP, UMP, Gambag, Kuantan, Pahang, Malaysia.
Computer Science & IT Electrical & Electronics Engineering

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A review on Video Classification with Methods, Findings, Performance, Challenges, Limitations and Future Work Md Shofiqul Islam; Sunjida Sultana; Uttam Kumar Roy; Jubayer Al Mahmud
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 6, No 2 (2020): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v6i2.18978

Abstract

In recent years, there has been a rapid development in web users and sufficient bandwidth. Internet connectivity, which is so low cost, makes the sharing of information (text, audio, and videos) more common and faster. This video content needs to be analyzed for prediction it classes in different purpose for the users. Many machines learning approach has been developed for the classification of video to save people time and energy. There are a lot of existing review papers on video classification, but they have some limitations such as limitation of the analysis, badly structured, not mention research gaps or findings, not clearly describe advantages, disadvantages, and future work. But our review paper almost overcomes these limitations. This study attempts to review existing video-classification procedures and to examine the existing methods of video-classification comparatively and critically and to recommend the most effective and productive process. First of all, our analysis examines the classification of videos with taxonomical details, the latest application, process, and datasets information. Secondly, overall inconvenience, difficulties, shortcomings and potential work, data, performance measurements with the related recent relation in science, deep learning, and the model of machine learning. Study on video classification systems using their tools, benefits, drawbacks, as well as other features to compare the techniques they have used also constitutes a key task of this review. Lastly, we also present a quick summary table based on selected features. In terms of precision and independence extraction functions, the RNN (Recurrent Neural Network), CNN (Convolutional Neural Network) and combination approach performs better than the CNN dependent method.
Co-Authors Jubayer Al Mahmud Sunjida Sultana Uttam Kumar Roy