# A Comprehensive Review of Computer Vision in Sports:
## 4 Literature Review
### Overview of Machine Learning/Deep Learning Techniques
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Studies in Various Sports**} \\ \hline
**Ref.** & **Problem Statement** & **Proposed Methodology** &
\begin{tabular}{c} **Precision and Performance** \\ **Characteristics** \\ \end{tabular} & \multicolumn{2}{c}{**Limitations and Remarks**} \\ \hline
[213] & \begin{tabular}{c} Court detection \\ using markers, \\ player detection, \\ and tracking using a \\ drone. \\ \end{tabular} & \begin{tabular}{c} Template Matching + \\ Particle Filter \\ \end{tabular} & \
\begin{tabular}{c} The proposed method achieves \\ better accuracy (94\%) in the case of \\ two overlapping players \\ \end{tabular} &
\begin{tabular}{c} As the overlapping of players, increases the \\ accuracy of detection and tracking decreases \\ due to similar features of players on the same \\ team. The method uses a template matching \\ algorithm, which can be replaced with a deep \\ learning-based state-of-art algorithm to \\ acquire better results. \\ \end{tabular} \\ \hline
[214] & \begin{tabular}{c} Target tracking \\ theory and analyses \\ its advantages in \\ video tracking. \\ \end{tabular} & \begin{tabular}{c} Mean Shift + Particle Filter \\ \end{tabular} & \
\begin{tabular}{c} Achieves better tracking accuracy \\ compared to existing algorithms \\ such as TMS and CMS algorithms. \\ \end{tabular} &
\begin{tabular}{c} If the target scales change then the tracking of \\ players fails due to the unchanged window \\ of the mean-shift algorithm. Furthermore, it \\ cannot track objects which are similar to the \\ background color. The accuracy of tracking \\ players can be improved by replacing them \\ with artificial intelligence algorithms. \\ \end{tabular} \\ \hline
[236] & \begin{tabular}{c} Automatically \\ generating a \\ summary of sports \\ video. \\ \end{tabular} & \begin{tabular}{c} 2D CNN + LSTM \\ \end{tabular} & \
\begin{tabular}{c} Describes a novel method for \\ automatic summarization of \\ user-generated sports videos and \\ demonstrated the results for \\ Japanese fencing videos. \\ \end{tabular} &
\begin{tabular}{c} The architecture can be improved by \\ fine-tuning in an end-to-end manner with a \\ larger dataset for illustrating potential \\ performance and also to evaluate in the \\ context of a wider variety of sports. \\ \hline
[227] & \begin{tabular}{c} Action Recognition \\ and classification \\ \end{tabular} & SVM & \
\begin{tabular}{c} Achieved an accuracy of 59.47\% on \\ the HMDB 51 dataset. \\ \end{tabular} &
\begin{tabular}{c} In cases where the object takes up most of the \\ frame, the human detector cannot completely \\ cover the body of the object. This leads to the \\ system missing movements of body parts \\ such as hands and arms. In addition, \\ recognition of similar movements is a \\ challenge for this architecture. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Cont. Figure 8: Block diagram of the road map to machine learning architecture selection and training. Figure 9: Block diagram of the road map to deep learning architecture selection and training.