# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 4 Experiments
### Results and Discussion  
For implementing the scratch model and the fine-tuned pretrained models in classifying the sports videos, the TBSV dataset was split into the train, test, and validation data with the ratio of 70:15:15. That means 70%, 15%, and 15% of the videos per class were randomly assigned for training, testing, and validating the models. Moreover, a specific number of frames is needed to be picked from each video to commence the task of classification. In this regard, we carried out an experiment using the scratch model to determine the right frame length, with 100 video samples in each class. In Figure 4, validation and test accuracy for frame length 10-25 are shown. From this figure, it can be observed that the model exhibits the best result, i.e., near about 93% validation accuracy and 91% test accuracy for the case of frame length 20. Hence, the frame length of 20 was deemed for further analysis and exploration in this ground. The dimension of the frames is resized to 128 \(\times\) 128 \(\times\) 3 for the sake of reducing computational complexity. The augmentation techniques horizontal flipping, rotation, width shifting, and height shifting are applied for better generalization and avoiding positional bias in the frames. Examples of resized and augmented frames are shown in Figure 5.  
As stated previously, we have used normalization for faster convergence of the model during training. The performance comparison of the scratch model on normalized and unnormalized data are represented in Table 2. The table shows that, with normalized training data, better performance has been achieved in less epoch relative to the unnormalized training data. After rigorous inspection and exploration with various combinations of hyperparameters, we have come up with our scratch model that showed a splendid performance in classifying the traditional Bangladeshi sports classes. This scratch model was formed by combining CNN with LSTM, compiled through Adam optimizer with 0.0001 learning rate and categorical cross-entropy loss function. Categorical cross-entropy is a type of loss function where the target values for multiclass classification are represented in a one-hot vector. The multinomial probability distribution provided by the Softmax activation function in the output layer is used by the categorical cross-entropy loss function for measuring the prediction error/loss of the model. This function computes the loss in the following manner:  
\[CCL\ =\ -\ log\ p(k) \tag{11}\]  
In the above equation, \(CCL\) refers to the categorical cross-entropy loss, and \(p(k)\) denotes the probabilistic value of the class \(k\) that is fired-up in the one-hot vector. However, the model is trained till 65 epochs with batch size of 64. The training and validation accuracy, as well as the loss curves of this scratch model, is depicted in Figure 6. This figure illustrates that after the 54 th epoch, the training accuracy stops increasing, whereas, at this epoch, we got the maximum validation accuracy, i.e., 93%. This observation refers to the fact that at the 54 th epoch, the model performs the best, and after this epoch, the model ceases learning. Moreover, Figure 7 represents the confusion matrix of this model applied to test data. It can be observed that a tiny amount of misclassification has been found in \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Training** & **Validation** & **Test** & **Total** \\  & **Accuracy** & **Accuracy** & **Accuracy** & **Epochs** \\ \hline Normalize Data & 99.30\% & 93\% & 91\% & 65 \\ Unnormalize & 96\% & 90\% & 88\% & 70 \\ Data & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effect of normalization on performance. Figure 4: Frame length vs. accuracy curve. some classes due to having some semantic feature similarity with the misclassified classes. Class-wise performance based on different evaluation metrics applied to test data for this model is summarized in Table 3. However, the overall performance is assessed through the F1 score throughout the experiments.