# A Comprehensive Review of Computer Vision in Sports:
## 4 Literature Review
### Cricket
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Studies in Cricket**} \\ \hline
**Refs.** & **Problem Statement** & **Proposed Methodology** &
\begin{tabular}{c} **Precision and Performance** \\ **Characteristics** \\ \end{tabular} & **Limitations and Remarks** \\ \hline
[117] & \begin{tabular}{c} Shot classification in \\ cricket. \\ \end{tabular} & CNN—Gated Recurrent Unit & \
\begin{tabular}{c} It is evaluated in terms of \\ precision, recall, and F1-score \\ and achieved 93.40\%, 93.10\%, and 93\% for 10 types of shots. \\ \end{tabular} &
\begin{tabular}{c} By incorporating unorthodox shots \\ which are played in t20 in the dataset \\ may improve the testing accuracy. \\ \end{tabular} \\ \hline
[125] & \begin{tabular}{c} Detecting the action of \\ the bowler in cricket. \\ \end{tabular} & VGG16-CNN & \
\begin{tabular}{c} It was evaluated in terms of \\ precision, recall, and F1-score \\ and the maximum average \\ accuracy achieved is 98.6\% for 13 \\ classes (13 types of bowling \\ actions). \\ \end{tabular} &
\begin{tabular}{c} Training the model with the dataset of \\ wrong actions can improve detection \\ accuracy. \\ \end{tabular} \\ \hline
[129] & \begin{tabular}{c} Movement detection \\ of the batsman in \\ cricket. \\ \end{tabular} & Deep-LSTM & \begin{tabular}{c} The model was evaluated in \\ terms of mean square error and \\ achieved a minimum error of \\ 1.107. \\ \end{tabular} & \
\begin{tabular}{c} The model was evaluated \\ terms of mean square error and \\ achieved a minimum error of \\ 1.107. \\ \end{tabular} &
\begin{tabular}{c} Decision tree classifier performance is \\ low due to the existence of a huge \\ number of trees. Therefore, a small \\ change in the decision tree may improve \\ the prediction accuracy. Extreme \\ Learning Machines have faced the \\ problem of overfitting, which can be \\ overcome by removing duplicate data in \\ the dataset. \\ \end{tabular} \\ \hline
[134] & \begin{tabular}{c} Prediction of \\ individual player \\ performance in cricket \\ \end{tabular} & \begin{tabular}{c} Efficient Machine \\ Learning Techniques \\ \end{tabular} & \
\begin{tabular}{c} The proposed algorithm \\ achieves a classification accuracy \\ of 93.73\% which is good \\ compared with traditional \\ classification algorithms. \\ \end{tabular} &
\begin{tabular}{c} Replacing machine learning techniques \\ with deep learning techniques may \\ improve the performance in prediction \\ even in the case of different \\ environmental conditions. \\ \end{tabular} \\ \hline
[114] & \begin{tabular}{c} Classification of \\ different batting shots \\ in cricket. \\ \end{tabular} & CNN & \
\begin{tabular}{c} The average classification in \\ terms of precession is 0.80, Recall \\ is 0.79 and F1-score is 0.79. \\ \end{tabular} &
\begin{tabular}{c} To improve the accuracy of classification, \\ a deep learning algorithm has to be \\ replaced with a better neural network. \\ \end{tabular} \\ \hline
[130] & \
\begin{tabular}{c} Outcome classification \\ task to create \\ automatic \\ commentary \\ generation. \\ \end{tabular} & CNN + LSTM &
\begin{tabular}{c} Maximum of 85\% of training \\ accuracy and 74\% validation \\ accuracy \\ accuracy leads to automatic commentary \\ generation in sports. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Studies in Cricket.