# On the Use of Deep Learning for Video Classification
## 1 Introduction
### Contribution  
There are several existing reviews and survey papers related to video classification in the scientific literature. Some of the recent works are summarized here in Table 1. However, these review papers do not include the recent state-of-art works, and they have some limitations. In the following text, the limitations and highlights of these works are discussed. A more recent review was done by [PERSON] [5]; this review covers very few methods for video classification, clustering, and tagging. However, the review provided is not comprehensive and lacks concise information, coverage of topic, datasets, analysis of state-of-art approaches, and research limitations;  
[PERSON] et al. [6] also conducted a recent review on video classification methods, and their review covered some recent video classification approaches and summary-based description of some recent works. This review also had some limitations including the missing analysis of recent state-of-art approaches and a very limited description of topics covered;  
[PERSON] et al. [7] recently conducted a systematic and good review on live sport video classification. This review covers most of the recent works in live sport video classification, including the tools, video interaction features, and feature extraction methods. This is a comprehensive review, but the findings are not summarized in tables for research gaps and advantages and disadvantages of existing methods for a quick review. Moreover, this review is more specific to live sport video classification;  
A recent review was also done by [PERSON] et al. [8]; in this review, they included all the methods for video classification, including deep learning. However, as the focus of review is not on deep learning approaches, these methods are therefore not completely covered in this review;  
[PERSON]. [PERSON] et al. [9] also conducted a recent systematic review; however, the focus of their review remained on human activity recognition;  
[PERSON]. [10] presented a concise review on video classification specific to deep learning methods. This review provides a good description on deep learning models, feature extraction tools, benchmark dataset, and comparison of existing methods for video classification. However, this review was conducted in the year 2016, and it does not cover the recent state-of-art deep learning methods; \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Year** & **Coverage** & **Highlights** & **Drawbacks** \\ \hline [PERSON]. [PERSON] [5] & 2020 & 2014–2019 & Video classification, tagging, and clustering. & Not comprehensive and lacks concise information. \\ [PERSON] et al. [6] & 2020 & 2001–2016 & Text, audio, and visual modalities for video & Missing analysis of recent \\  & & & classification. & state-of-art approaches. \\ [PERSON] et al. [7] & 2020 & 2012–2019 & Live sport video classification. & More specific to live sport \\ [PERSON] et al. [8] & 2021 & 2004–2020 & Machine learning approaches for video classification. & Focus of review is not on \\ [PERSON] et al. [9] & 2021 & 2015–2020 & Human activity recognition using deep learning. & Focus only on the human \\ This study & 2022 & 2000–2022 & Comprehensive deep learning review for & activity recognition. \\  & & & video classification. & - \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of recent related works. [PERSON] [11] conducted a simple review on video classification methods; however, the techniques covered in this review are not well described, and the review also lacks in the description of research gaps, benchmark datasets, limitations of existing methods, and performance metrics. In contrast to the existing reviews on classification of videos, this paper provides a more comprehensive, concise, and up-to-date review of deep learning approaches for video classification. In this current review, most of the recent state-of-art contributions related to the topic are analyzed and critically summarized.