# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 3 Results and Discussion
### Performance Evaluation Using Different Classifiers
#### 3.5.2 Classification Using Support Vector Machine (SVM)
For SVM classification, we obtained the average accuracy of 73.05% using LBP descriptor. The accuracy against each shot category is provided in Table 3. Similarly, we obtained an average accuracy of 74.46% for LTrP descriptor with SVM classifier. The accuracy for each shot category is presented in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline
**Videos** & **Crowd Shot** & **Long Shots** & **Medium Shots** & **Close-Up Shots** \\ \hline Video 1 & 77.49 & 74.32 & 72.32 & 73.21 \\ Video 2 & 78.91 & 73.45 & 73.45 & 74.67 \\ Video 3 & 77.65 & 75.62 & 72.78 & 73.91 \\ Video 4 & 77.89 & 73.32 & 73.32 & 73.32 \\ Video 5 & 76.57 & 75.4 & 72.11 & 72.11 \\ Video 6 & 76.66 & 74.31 & 71.25 & 73.45 \\ Video 7 & 78.64 & 74.81 & 70.19 & 74.56 \\ Video 8 & 79.49 & 75.37 & 72.35 & 73.44 \\ Video 9 & 76.34 & 73.67 & 71.23 & 72.16 \\ Video 10 & 76.34 & 76.34 & 72.31 & 73.71 \\ \hline Average & 77.59\% & 74.67\% & 72.12\% & 73.45\% \\ \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy using SVM (linear kernel) with LTrP features. \begin{table}
\begin{tabular}{c c c c c} \hline
**Videos** & **Crowd Shot** & **Long Shots** & **Medium Shots** & **Close-Up Shots** \\ \hline Video 1 & 77.89 & 72.12 & 70.33 & 72.34 \\ Video 2 & 76.51 & 72.56 & 70.23 & 73.45 \\ Video 3 & 78.34 & 73.57 & 70.75 & 73.41 \\ Video 4 & 76.88 & 72.53 & 70.65 & 72.35 \\ Video 5 & 72.34 & 71.91 & 70.55 & 72.85 \\ Video 6 & 77.82 & 72.85 & 70.5 & 71.23 \\ Video 7 & 76.43 & 73.44 & 70.46 & 72.46 \\ Video 8 & 76.82 & 72.56 & 70.23 & 73.83 \\ Video 9 & 76.23 & 71.23 & 70.43 & 72.76 \\ Video 10 & 77.21 & 70.34 & 70.34 & 73.62 \\ \hline Average & 76.64\% & 72.31\% & 70.45\% & 72.8\% \\ \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy using SVM (linear kernel) with LBP features. The experiments reveal that the combination of LTrP with SVM provides better accuracy as compared to LBP with the SVM. Experiments signify that crowd shots were categorized effectively in the remaining shots, which is attributed to the fact that the crowd shots contain less dominant grass field color as compared to other categories. We also used different kernels of SVM like quadratic, Multi-layer Perception (MLP) and radial basis function (RBF) during experimentation. MLP is the most popular kernel of SVM, it is the class of feed forward neural networks and is used when response variable is categorical. RBF kernel is used when there is no prior knowledge about the data as it induces Gaussian distributions. Each point in the RBF kernel becomes a probability density function of normal distribution. It is a sort of rough distribution of data. Whereas quadratic kernel is used to induce a polynomial combinations of the features, working with bended decision boundaries. Quadratic and RBF kernel are expressed in Equations (23) and (24), respectively. \[K(p,q)=\left(p^{t}q+e\right)^{d} \tag{22}\]  
where p and q represent input space vectors that are generated from training or validation sets. Note that e \(\geq\) 0 is a free parameter that influences higher order versus lower order terms in the polynomial. \[r(s)=\ \sum_{i=1}^{N}v_{i}\mathcal{O}(||s-s_{i}||) \tag{23}\]  
where r(s) is the approximating function, which is expressed as a sum of N radial basis functions, s\({}_{\text{i}}\) is the center value weighted by v\({}_{\text{j}}\). v\({}_{\text{j}}\) is the estimated weight. The results obtained on LBP features with SVM using different kernel functions (quadratic, radial basis function (RBF) and MLP kernel) for shot classification are presented in Table 5.  
Similarly, the results of LTrP features with SVM using different kernel functions for shot classification are presented in Table 6.  
It has been observed from Tables 3-6 that linear SVM provides better performance as compared to quadratic, MLP, and RBF kernels. In addition, there is a slight difference in the performance accuracy between RBF and quadratic kernels, however, we received a very low accuracy rate for MLP kernel.