# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 5 Conclusions  
In this paper, a combined network consisting of CNN and RNN incorporated with a multiclass classifier is proposed to classify traditional Bangladeshi sports videos. This task is quite challenging, as it requires the capture and processing of both spatial and temporal features. However, the main barrier to this task is the scarcity of the Traditional Bangladeshi Sports Video (TBSV) dataset. Hence, we have built a new traditional Bangladeshi sports video dataset containing 500 sports videos of five categories: [PERSON], Kabaddi, [PERSON], [PERSON], and [PERSON], which is one of the key contributions of this work. Indeed, our research can serve a noble cause of reviving the lost traditional Bangladeshi sports and thus spreading their cultural significance. To accomplish the classification task on the TBSV dataset, a scratch network is developed by incorporating CNN with LSTM, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classes**} & **Mean** & **Precision** & **Sensitivity** & **Specificity** & **F1 Score** \\  & **Accuracy (\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline [PERSON] & & 94 & 100 & 98 & 97 \\ [PERSON] & & 100 & 93 & 100 & 97 \\ [PERSON] & 99 & 100 & 100 & 100 & 100 \\ [PERSON] & & 100 & 100 & 100 & 100 \\ [PERSON] & & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Class-wise performance of Model 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**KTH**} & \multicolumn{2}{c}{**UCF-11**} & \multicolumn{2}{c}{**UCF Sports**} \\ \hline
**Method** & **Accuracy** & **Method** & **Accuracy** & **Method** & **Accuracy** \\ \hline [PERSON] [31] & 90.34\% & [PERSON] et al. [32] & 89.7\% & [PERSON] et al. [33] & 82.4\% \\ [PERSON] and [PERSON] [20] & 93.33\% & [PERSON] et al. [21] & 91.2\% & [PERSON] et al. [18] & 89.01\% \\ [PERSON] et al. [34] & 95.80\% & [PERSON] et al. [16] & 92.84\% & [PERSON] and [PERSON] [20] & 90\% \\ [PERSON] et al. [18] & 96.30\% & [PERSON] et al. [19] & 94.12\% & [PERSON] et al. [30] & 90\% \\ Our Proposed Model & 97\% & Our ProposedModel & 95.6\% & Our Proposed Model & 94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison with other methods. which showed decent accuracy of 91% over the test data. On the other hand, an experiment was conducted with the transfer learning approach through fine-tuning VGG16 and VGG19 networks. However, the proposed model is comprised of the fine-tuned VGG19 network integrated with LSTM, which shows promising achievement on the TBSV dataset and some benchmark datasets of human activity: KTH, UCF-11, UCF-101, and UCF Sports. Additionally, the experimental results testify that our proposed model can be used in sports and human activity recognition tasks to a great extent. However, there is still space for improvement in this field. In the future, we aim to work with some more traditional Bangladeshi sports classes to enhance the extensiveness of this work. Furthermore, for better generalization, some more augmentation techniques are intended to use. Some ensemble models are aimed to implement in future, as in [22], with in-built regularization technique. Additionally, we aspire to explore the fine tuning of some other pretrained models like Resnet, Inception, Xception, etc., and analyze their potency in this ground. Conceptualization: K.D. ; data curation: M.S.S. ; formal analysis: M.S.S. ; investigation: M.S.S. ; methodology: M.S.S. ; software: M.S.S. ; supervision: K.D. ; validation: M.S.S. ; visualization: M.S.S. ; writing--original draft: M.S.S. ; writing--review and editing: K.D., P.K.D. and T.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors have constructed a novel dataset named Traditional Bangladeshi Sports Video (TBSV) dataset for the experiment, which is available on request from the corresponding author.