# A Comprehensive Review of Computer Vision in Sports:
## 4 Literature Review
### Basketball
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & **Studies in Basketball** & \\ \hline
**Ref.** & **Problem Statement** & **Proposed Methodology** &
\begin{tabular}{c} **Precision and Performance** \\ **Characteristics** \\ \end{tabular} & **Limitations and Remarks** \\ \hline
[58] & \
\begin{tabular}{c} Predicting line-up \\ performance of \\ baskeuthal players by \\ analyzing the \\ situation of the field. \\ \end{tabular} & RNN + NN &
\begin{tabular}{c} At the point guard (pg) position \\ 4 candidates were detected and at \\ the center (c) position 3 candidates \\ were detected. The total score of pg \\ candidates is 13.67, 12.96, 13.42, 10.39, and where the total score of c \\ candidates is 10.21, 14.08, and 13.48, respectively. \\ \end{tabular} & **Limitations and Remarks** \\ \hline
[32] & \begin{tabular}{c} Multiplayer tracking \\ in basketball videos \\ \end{tabular} & \begin{tabular}{c} YOLOv3 + Deep-SORT, \\ Faster-RCNN + \\ Deep-SORT, YOLOv3 + \\ DeepMOT, Faster-RCNN + \\ DeepMOT, JDE \\ \end{tabular} & \
\begin{tabular}{c} Faster-RCNN provides better \\ accuracy than YOLOv3 among \\ baseline detectors. The joint \\ Detection and Embedding method \\ performs better in the accuracy of \\ tracking and computing speed \\ among multi-object tracking \\ methods. \\ \end{tabular} &
\begin{tabular}{c} Tracking in specific areas such as severe \\ occlusions and improving detection \\ precision improves the accuracy and \\ computation speed. By adopting frame \\ and accuracy, it can achieve \\ comprehensive performance, which may \\ be an alternative solution. \\ \end{tabular} \\ \hline
[40] & \begin{tabular}{c} Recognizing the \\ referee signals from \\ real-time videos in a \\ basketball game. \\ \end{tabular} & \begin{tabular}{c} \\ HOG + SVM, LBP + SVM \\ \end{tabular} & \
\begin{tabular}{c} Achieved an accuracy of 95.6\% for \\ referee signal recognition using local \\ binary pattern features and SVM \\ classification. \\ \end{tabular} &
\begin{tabular}{c} In the case of a noisy environment, a \\ significant chance of occlusion, an \\ unusual viewing angle, and/or \\ variability of gestures, the performance \\ of the proposed method is not consistent. \\ Detecting jersey color and eliminating all \\ other detected elements in the frame can \\ be the other solution to improve the \\ accuracy of referee signal recognition. \\ \end{tabular} \\ \hline
[30] & \begin{tabular}{c} Event recognition in \\ basketball videos \\ \end{tabular} & CNN & \
\begin{tabular}{c} mAP for group activity recognition is \\ 72.1\% \\ \end{tabular} &
\begin{tabular}{c} The proposed model can recognize the \\ global movement in the video. By \\ recognizing the local movements, the \\ accuracy can be improved. \\ \end{tabular} \\ \hline
[59] & \begin{tabular}{c} Analyzing the \\ behavior of the player. \\ \end{tabular} & CNN + RNN & \
\begin{tabular}{c} Achieved an accuracy of 76.5\% for \\ four types of actions in basketball \\ videos. \\ \end{tabular} &
\begin{tabular}{c} The proposed model gives less accuracy of \\ for actions such as passing and fouling. \\ This also gives less accuracy of \\ recognition and prediction on the test \\ dataset compared to the validation \\ dataset. \\ \end{tabular} \\ \hline
[33] & \begin{tabular}{c} Tracking ball \\ movements and \\ classification of \\ players in a basketball game \\ \end{tabular} & YOLO + Joy2019 & \
\begin{tabular}{c} Jersey number recognition in terms \\ of Precision achieved is 74.3\%. \\ Player recognition in terms of Recall \\ achieved 89.8\%. \\ \end{tabular} &
\begin{tabular}{c} YOLO confuses the overlapped image for \\ a single player. In the subsequent frame, \\ the tracking ID of the overlapped player \\ is exchanged, which causes wrong player \\ information to be associated with the \\ identified box. \\ \end{tabular} \\ \hline
[29] & \begin{tabular}{c} Event classifications in \\ basketball videos \\ \end{tabular} & CNN + LSTM & \
\begin{tabular}{c} The average accuracy using a \\ two-stage event classification scheme \\ achieved 60.96\%. \\ \end{tabular} &
\begin{tabular}{c} Performance can be improved by \\ introducing information such as \\ individual player pose detection and \\ player location detection \\ \end{tabular} \\ \hline
[49] & \
\begin{tabular}{c} Classification of \\ different defensive \\ strategies of basketball \\ players, particularly \\ when they deviate \\ from their initial \\ defensive action. \\ \end{tabular} &
\begin{tabular}{c} Crossidered only two defensive strategies \\ ’switch’ and ’trap’ involved in Basketball \\ In addition, the alternative method of \\ labeling large Spatio-temporal datasets \\ will also lead to better results. Future \\ research may also consider other \\ defensive strategies such as pick-and-roll \\ and pick-and-pop. \\ \end{tabular} \\ \hline
[38] & \begin{tabular}{c} Basketball trajectory \\ prediction based on \\ real data and \\ generating new \\ trajectory samples. \\ \end{tabular} & \
\begin{tabular}{c} The proposed method performed \\ well in terms of convergence rate \\ and final AUC (91\%) and proved \\ deep learning models perform better \\ than conventional models (e.g., \\ GLM, GBM). \\ \end{tabular} &
\begin{tabular}{c} To improve the accuracy time series the \\ prediction has to consider. By \\ considering factors such as player \\ cooperation and defense when predicting \\ NBA player positions, the performance \\ of the model can be improved. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}