A review on Video Classification with Methods, Findings, Performance, Challenges, Limitations and Future Work

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 class 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 existing review papers on video classification, but they have some limitations such as limitation of 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, 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.