# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 3 Results and Discussion
### Performance Evaluation Using Different Classifiers
#### 3.5.4 Classification Using KNN  
We also implemented and tested the shot classification performance on our cricket and soccer video dataset using K-Nearest Neighbor (K-NN) classifier. In KNN classification, an object is classified according to majority vote of its neighbors, with the object assigned to the most common class among its k nearest neighbors. We performed this experiment on different values of k and obtained the best results with k = 5, therefore, the value for K in KNN is set to five in our experiments. Nearest neighbors are computed by calculating the Euclidean distance formula. The results obtained using the KNN are provided in Table 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Videos** & **Accuracy** & **Precision** & **Recall** & **Error** \\ \hline Video 1 & 73.21 & 73.21 & 76.32 & 26.79 \\ Video 2 & 74.67 & 72.13 & 76.34 & 25.33 \\ Video 3 & 73.91 & 73.91 & 76.58 & 26.09 \\ Video 4 & 73.32 & 73.86 & 75.49 & 26.68 \\ Video 5 & 72.11 & 72.69 & 76.45 & 27.89 \\ Video 6 & 73.45 & 73.45 & 76.77 & 26.55 \\ Video 7 & 74.56 & 71.73 & 77.49 & 25.44 \\ Video 8 & 73.44 & 73.44 & 76.45 & 26.56 \\ Video 9 & 72.16 & 72.16 & 75.86 & 27.84 \\ Video 10 & 73.71 & 70.12 & 76.21 & 26.29 \\ \hline Average & **73.45\%** & **72.67\%** & **76.39\%** & **26.55\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance evaluation using extreme learning machine classifier with local binary pattern features. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Videos** & **Accuracy** & **Precision** & **Recall** & **Error** \\ \hline
**Video 1** & 76.54 & 77.43 & 79.45 & 23.46 \\
**Video 2** & 77.23 & 77.23 & 77.49 & 22.77 \\
**Video 3** & 75.21 & 75.21 & 77.48 & 24.79 \\
**Video 4** & 75.95 & 75.95 & 77.48 & 24.05 \\
**Video 5** & 74.28 & 77.84 & 78.81 & 25.72 \\
**Video 6** & 75.11 & 75.17 & 77.34 & 24.89 \\
**Video 7** & 74.56 & 76.39 & 78.39 & 25.44 \\
**Video 8** & 76.57 & 76.57 & 78.91 & 23.43 \\
**Video 9** & 75.66 & 77.65 & 77.65 & 24.34 \\ Video 10 & 74.52 & 74.85 & 75.97 & 25.48 \\ \hline Average & 75.56\% & 76.43\% & 77.89\% & 24.44\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance evaluation using extreme learning machine classifier with local tetra pattern features. 3.5.5. Classification Using Centroid Displacement-Based K-Nearest Neighbors (CDNN)  
We also implemented and tested the shot classification performance on our cricket and soccer video dataset using centroid displacement-based K-Nearest Neighbors (CDNN) [29] classifier. In CDNN classification, along with the distance parameter, an integral learning component that learns the weight of the view is added which helps in classifying new shots in the test dataset. The value of k for CDNN is set to five for our experiments as we obtained the optimal results on this value after checking the classifier performance on different values of k. The results obtained using the CDNN are far better than SVM and ELM classifiers, but lesser than our proposed method. The results are presented in Table 10.  
Performance comparison of the proposed method with SVM, KNN, CDNN, ELM, and standard CNN classifiers are shown in Figure 8. The proposed method achieves an average accuracy of 94.07% in comparison with CDNN, CNN, KNN, ELM and SVM that provides 92.5%, 91.67%, 91.75%, 74.50% and 69.45%, respectively. From the results in Figure 8, it can be clearly observed that the proposed method performs far superior, as compared to SVM, ELM, and marginally better than KNN, CDNN, and standard CNN for shot classification. Therefore, we can argue that the proposed method is very effective in terms of shot classification of sports videos.