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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


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.


Video Classification; Machine learning; Deep learning; Video; Video classification

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Brezeale, D. and D.J. Cook, Automatic video classification: A survey of the literature. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 38, no. 3, p. 416-430, 2008. DOI:

Wu, Z., et al., Deep learning for video classification and captioning, in Frontiers of multimedia research, 3122867 p. 3-29, 2017. DOI:

Ren, Q., et al., A Survey on Video Classification Methods Based on Deep Learning. DEStech Transactions on Computer Science and Engineering, cisnrc, 33301 .p. 1-7, 2019. DOI:

Anushya, A., VIDEO TAGGING USING DEEP LEARNING: A SURVEY, International Journal of Computer Science and Mobile Computing,Vol.9 Issue.2,pg. 49-55,2020.

Rani, P., J. Kaur, and S. Kaswan, Automatic Video Classification: A Review. EAI Endorsed Transactions on Creative Technologies, ,7(24), p. 163996,2020). DOI:

Li, Y., C. Wang, and J. Liu, A Systematic Review of Literature on User Behavior in Video Game Live Streaming. International Journal of Environmental Research and Public Health, vol. 17, no. 9, p. 3328,2020. DOI:

Zhen, M., et al. Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation. in European Conference on Computer Vision. Springer, LNCS, volume 12372,pp 445-46,2020. DOI:

Li, Z., R. Li, and G. Jin, Sentiment Analysis of Danmaku Videos Based on Naïve Bayes and Sentiment Dictionary. IEEE Access, vol. 8, p. 75073-75084,2020. DOI:

Ruz, G.A., P.A. Henríquez, and A. Mascareño, Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Future Generation Computer Systems, 106: p. 92-104,2020. DOI:

Xu, Q., et al., Aspect-based sentiment classification with multi-attention network. Neurocomputing, vol. 388, p. 135-143, 2020. DOI:

Bibi, M., et al., A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis. IEEE Access, Vol. 8, p. 68580 - 68592,2020. DOI:

Sailunaz, K. and R. Alhajj, Emotion and sentiment analysis from Twitter text. Journal of Computational Science, vol. 36, p. 101003, 2020. DOI:

Peng, T., et al., Video Classification Based On the Improved K-Means Clustering Algorithm. E&ES, vol. 440, no. 3, p. 032060,2020. DOI:

Li, X. and S. Geng, Research on sports retrieval recognition of action based on feature extraction and SVM classification algorithm. Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5797-5808, 2020. DOI:

Alomari, E., R. Mehmood, and I. Katib, Sentiment Analysis of Arabic Tweets for Road Traffic Congestion and Event Detection, in Smart Infrastructure and Applications, Springer. p. 37-54, 2020. DOI:

Ren, R., D.D. Wu, and T. Liu, Forecasting stock market movement direction using sentiment analysis and support vector machine. IEEE Systems Journal, vol. 13, no. 1, p. 760-770, 2020.DOI:

Yadav, A. and D.K. Vishwakarma, A unified framework of deep networks for genre classification using movie trailer. Applied Soft Computing, vol. 96: p. 106624, 2020. DOI:

Parameswaran, S., et al., Exploring Various Aspects of Gabor Filter in Classifying Facial Expression, in Advances in Communication Systems and Networks, Springer. p. 487-500, 2020. DOI:

Hauptmann, A., et al., with the Informedia Digital Video Library System, MULTIMEDIA '94,Pages 480–481, 1994.

Warner, W. and J. Hirschberg. Detecting hate speech on the world wide web. in Proceedings of the second workshop on language in social media. 2012. Association for Computational Linguistics. (LSM 2012), pages 19–26, 2012.

Li, C., et al., Infant Facial Expression Analysis: Towards A Real-time Video Monitoring System Using R-CNN and HMM. IEEE Journal of Biomedical and Health Informatics, 9254091, pp 1-12, 2020. DOI:

Shen, J., et al., Towards an efficient deep pipelined template-based architecture for accelerating the entire 2D and 3D CNNs on FPGA. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2019. 1442 - 1455,Vol. 39, no. 7, July 2020. DOI:

Meng, B., X. Liu, and X. Wang, Human action recognition based on quaternion spatial-temporal convolutional neural network and LSTM in RGB videos. Multimedia Tools and Applications, vol. 77, no. 20, p. 26901-26918,2018. DOI:

Yang, H., et al., Asymmetric 3d convolutional neural networks for action recognition. Pattern recognition, vol. 85, p. 1-12, 2019. DOI:

Kar, A., et al. Adascan: Adaptive scan pooling in deep convolutional neural networks for human action recognition in videos. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. (CVPR), pp. 3376-3385,2017. DOI:

Cho, K., et al., Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, p. 1-45, 2014. DOI:

Shofiqul, M.S.I., N. Ab Ghani, and M.M. Ahmed, A review on recent advances in Deep learning for Sentiment Analysis: Performances, Challenges and Limitations. COMPUSOFT: An International Journal of Advanced Computer Technology, vol. 9, no. 7, p. 3768-3776, 2020.

Kalra, G.S., R.S. Kathuria, and A. Kumar. YouTube Video Classification based on Title and Description Text. in 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). 2019. IEEE. ICCCIS48478,p. 8974514,2019. DOI:

Yuan, F., et al., End-to-end video classification with knowledge graphs. arXiv preprint arXiv:1711.01714, 2017. 1711.01714, pp 1-9, 2017.

Voulodimos, A., et al., Deep learning for computer vision: A brief review. Computational intelligence and neuroscience, 7068349, pp 1-13, 2019. DOI:

Sargano, A.B., P. Angelov, and Z. Habib, A comprehensive review on handcrafted and learning-based action representation approaches for human activity recognition. applied sciences, vol. 7, no. 1, p. 110,2017. DOI:

Elboushaki, A., et al., MultiD-CNN: A multi-dimensional feature learning approach based on deep convolutional networks for gesture recognition in RGB-D image sequences. Expert Systems with Applications, vol. 139: p. 112829, 2020. DOI:

Huiqun, Z., W. Hui, and W. Xiaoling. Application research of video annotation in sports video analysis. in 2011 International Conference on Future Computer Science and Education.IEEE, 6041660, p. 1-5, 2011. DOI:

Herath, S., M. Harandi, and F. Porikli, Going deeper into action recognition: A survey. Image and vision computing, vol. 60, p. 4-21, 2017. DOI:

Chen, H., et al., Action recognition with temporal scale-invariant deep learning framework. China Communications, vol. 14, no. 2, p. 163-172, 2017. DOI:

Peng, X., et al. Action recognition with stacked fisher vectors. in European Conference on Computer Vision, Springer. ECCV,2014,pp 581-595, 2014. DOI:

Lan, Z., et al. Beyond gaussian pyramid: Multi-skip feature stacking for action recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition, (CVPR), pp. 204-212, 2015.

Dalal, N., B. Triggs, and C. Schmid. Human detection using oriented histograms of flow and appearance. in European conference on computer vision, Springer. ECCV, p. 428-441, 2006. DOI:

Asadi-Aghbolaghi, M., et al. A survey on deep learning based approaches for action and gesture recognition in image sequences. in 2017 12th IEEE international conference on automatic face & gesture recognition (FG 2017), IEEE. 7961779, p. 1-8, 2017. DOI:

Yang, X., P. Molchanov, and J. Kautz. Multilayer and multimodal fusion of deep neural networks for video classification. in Proceedings of the 24th ACM international conference on Multimedia, 2964297, p. 978–987. 2016. DOI:

Yue-Hei Ng, J., et al. Beyond short snippets: Deep networks for video classification. in Proceedings of the IEEE conference on computer vision and pattern recognition,(CVPR), p. 4694-4702, 2015.

Dvir, A., et al., Encrypted Video Traffic Clustering Demystified. Computers & Security, Volume 96, p. 101917, 2020. DOI:

Yin, D., et al., Detection of harassment on web 2.0. Proceedings of the Content Analysis in the WEB, 2: p. 1-7, 2009.



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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika
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