# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 4 Experiments
### Results and Discussion
Figure 8: The feature maps produced by the convolutional part of Model 9 of (**a**) Boli Khela, (**b**) Kabaddi, (**c**) Kho Kho, (**d**) Lathi Khela, and (**e**) Nouka Baich. Moreover, the Nouka Baich is a traditional boat rowing sport mostly played during the rainy season on water surfaces. In this paper, the feature maps for each class represented in Figure 8 are organized according to feature maps of the lower-level to higher-level layers of the convolutional part of Model 9. From the feature maps of lower-level layers, it can be observed that our convolutional architecture primarily grabs the surrounding of the playground, key sports equipment, and characteristics of players of each sport as features. As the layers go deeper, the extracted features become more encoded and lack human readability. The training and validation accuracy, along with the loss curves of the proposed model, is represented in Figure 9. It can be noticed from this figure that the maximum training accuracy of the model was attained at the 27 th epoch, where validation accuracy was the maximum, i.e., 99%. The confusion matrix of this proposed model, i.e., Model 9, is portrayed in Figure 10. The figure shows that, due to having some similar semantic characteristics, only a test sample in \"Kabaddi\" class was misclassified as \"Boli Khela\", which proves this model's efficiency to a great extent. To get more insight into Model 9's performance, class-wise values of evaluation metrics are demystified in Table 6.  
Figure 10: Confusion matrix for Model 9. Figure 9: Performance of Model 9. Driven by the impressive performance of Model 9 over the TBSV dataset, we extended our research work by employing Model 9 in the classification task on the four most prominent datasets: KTH, UCF-11, UCF-101, and UCF Sports. For all the datasets, the split ratio is considered as 80:20 (i.e., 80% of the video data is selected for training, and 20% for testing the network randomly). For consistency, 10 frames per video are considered in the experiment. Table 7 represents the performance comparison of our proposed model over KTH, UCF-11, and UCF Sports datasets with some recent works. On the KTH dataset, our model achieved better accuracy, i.e., 97%, than the approach of [PERSON] et al. [18], which was based on a hybrid deep learning. In the case of the UCF-11 dataset, the proposed model achieved 95.6% accuracy, which surpasses the performance of the method of [PERSON] et al. [19], which was based on attention mechanism-based CNN-LSTM network. Additionally, after training the proposed model over the UCF Sports dataset, 94% test accuracy has been acquired, proving this model's worthiness relative to the DCNN-LSTM based method of [PERSON] et al. [30] and deep convolution network-based PCANet with encoding method of [PERSON] [20]. Moreover, our proposed model achieved 96% accuracy on UCF-101 dataset and outperforms [18], which also recognizes 66 classes of this dataset by hybrid deep learning model and achieved 89.30% accuracy. Moreover, in [35], 81.93% accuracy was obtained in recognizing 101 classes of this dataset using deep multimodal fusion features. Relative to this, because of our limitations of hardware and memory configuration, we have considered only 66 classes of two categories (sports and body-motion only) in UCF-101 dataset and achieved 96% accuracy.