# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 4 Experiments
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classes**} & **Mean** & **Precision** & **Sensitivity** & **Specificity** & **F1 Score** \\  & **Accuracy (\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline [PERSON] & & 93 & 87 & 98 & 90 \\ [PERSON] & & 94 & 100 & 98 & 97 \\ [PERSON] & 91 & 93 & 93 & 98 & 93 \\ [PERSON] & & 76 & 87 & 93 & 81 \\ [PERSON] & & 100 & 87 & 100 & 93 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Class-wise performance of the scratch model. Figure 5: Examples of resized and augmented frames: (**a**) original frame, (**b**) resized frame, (**c**) horizontally flipped frame, (**d**) rotated frame, (**e**) width shifted frame, (**f**) and height shifted frame. Moreover, we conducted an experiment with some of the leading pretrained models combined with LSTM. The performance comparison of these models over the TBSV dataset is rendered in Table 4. From this table, it can be observed that the VGG16 and VGG19 model outperforms the others in classifying TBSV classes. Furthermore, we have also explored fine-tuned VGG16 and VGG19 networks incorporated with LSTM. Various network configurations experimented with by the fine-tuning task are illustrated in Table 5. All the models are compiled using Adam optimizer with 0.0001 learning rate and categorical cross-entropy loss function, trained until 30 epochs with the batch size of 64. However, GPU memory constraint is a crucial issue in training deep learning models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pretrained Models** & \
\begin{tabular}{c} **Hidden Units in** \\ **LSTM Layer** \\ \end{tabular} &
\begin{tabular}{c} **Neurons in Dense** \\ **Layer (0.2 Dropout)** \\ \end{tabular} & **Test Accuracy (\%)** \\ \hline ResNet152 v2 & & & 85 \\ Inception v3 & & 86 \\ Xception & 128 & 128 & 88 \\ VGG16 & & & 90 \\ VGG19 & & & 92 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of some pretrained models on the TBSV dataset. Figure 6: Performance of scratch model. Figure 7: Confusion matrix for the scratch model. However, from this table, it can be noticed that the proposed model, i.e., Model 9 with retraining last eight layers of VGG19 integrated with LSTM and fully connected layer, achieved the maximum F1 score of 99%, which is the ultimate evaluation metric considered in this paper. In this study, dropout rate of 0.2 is applied to the fully connected layer to reduce overfitting of the model. The rate 0.2 means 20 neurons out of each 100 from this layer are ignored while training. Moreover, our proposed model contains 18,436,363 weights/parameters, whereas Model 7 contains 24,301,899 weights/parameters, in which we fine-tuned all layers of VGG-19. Here, it can be observed that our proposed model contains 5,865,536 fewer weights/parameters than Model 7. The feature maps produced by the convolutional part of Model 9 from a frame of videos of each class are depicted in Figure 8.  
The feature map provides a glimpse of how the input data is dissected into various filters of the model. However, deep neural networks work like a black-box in extracting features from the input. Higher-level layers produce feature maps that are more abstract and opaque to humans than lower-level layers in the model. They encode higher-level features that are difficult to extract and present in a human-readable way [29]. Therefore, the deep learning models extract higher-level features that are mystical and more immense in amount than the features considered by mere humans. There are certain key distinguishable features in each sports class. Boli Khela is mostly played between two competitors in a sandy wrestling playground, whereas the Kabaddi playground is divided into two halves occupied by two opposing teams. However, the chasing team in Kho Kho assembles in eight squares in the rectangle playground's central lane. Additionally, the Lathi Khela is a stick fighting sport played between rival groups.