# Sports Video Classification Method Based on Improved Deep Learning
## 3 Results  
In this section, we present examples of video data classification using the model proposed in our study (as shown in Figure 5). We demonstrate the classification results for inputs using both high-resolution and low-resolution datasets, and compare these with other traditional models. Finally, we use a Confusion Matrix to illustrate the strong generalization capabilities of our classification algorithms.  
In this study, we compared our proposed algorithm with other renowned models in the SPORTS1 m and a bespoke dataset, which encompass sports videos of varying resolutions. Based on this, we elucidated the effectiveness of our proposed algorithm. Initially, the evaluation was conducted on the SPORTS1 m dataset. The corresponding results can be referred to in Table 1 and Figure 6.  
In light of the empirical findings presented, it is evident that our proposed algorithm demonstrates superior performance in comparison to alternative models when evaluated on a high-resolution dataset. Specifically, the method achieved a precision of 0.9718, an accuracy rate of 0.9804, an F-score of 0.9761, and a recall rate of 0.9723.  
Next, we evaluate the models on a Low-Resolution Dataset, specifically curated for sports video classification. The results are as follows Table 2, Figures 6 and 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Accuracy** & **F-Score** & **Recall** \\ \hline DenseNet & 0.9690 & 0.9730 & 0.9670 & 0.9651 \\ VggNet-16 & 0.9338 & 0.9631 & 0.9238 & 0.9140 \\ Inception v3 & 0.9531 & 0.9781 & 0.9654 & 0.9677 \\ ResNet-50 & 0.9380 & 0.9645 & 0.9528 & 0.9681 \\ Proposed method & 0.9718 & 0.9804 & 0.9761 & 0.9723 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification results of different classification algorithms on the high-resolution dataset.  
Figure 5: Example of classification results of proposed algorithm.  
Figure 6: Accuracy and loss curves of classification on the high-resolution dataset.  
Based on the empirical data presented, it is manifestly evident that our proposed algorithm surpasses its peers when evaluated on the low-resolution dataset, excelling in metrics such as precision, accuracy, F-score, and recall. Concurrently, the Confusion Matrix derived from the Validation Set accentuates the robust generalization prowess of our model (as shown in Figure 8), potentially attributable to the incorporation of the deblurring algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Accuracy** & **F-Score** & **Recall** \\ \hline DenseNet & 0.8627 & 0.8763 & 0.8675 & 0.8592 \\ VggNet-16 & 0.8532 & 0.8604 & 0.8561 & 0.8523 \\ Inception v3 & 0.8571 & 0.8682 & 0.8634 & 0.8605 \\ ResNet-50 & 0.8583 & 0.8651 & 0.8602 & 0.8574 \\ Proposed method & 0.8725 & 0.8803 & 0.8752 & 0.8701 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification results of different classification algorithms on the low-resolution dataset.  
Figure 8: Confusion Matrix under the Validation Set of the low-resolution dataset.  
Figure 7: Accuracy and loss curves of classification on the low-resolution dataset.  
In conclusion, the empirical evidence underscores that our proposed sports video classification algorithm consistently outshines its contemporaneous, well-regarded counterparts across both the SPORTS1 m and bespoke datasets. Such findings unequivocally attest to the robustness and efficacy of our model in the realm of sports video classification.