# A Comprehensive Review of Computer Vision in Sports:
## 4 Literature Review
### Miscellaneous
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Studies in Various Sports**} \\ \hline
**Ref.** & **Problem Statement** & **Proposed Methodology** &
\begin{tabular}{c} **Precision and Performance** \\ **Characteristics** \\ \end{tabular} & **Limitations and Remarks** \\ \hline
[211] & \begin{tabular}{c} Beach sports image \\ recognition and \\ classification. \\ \end{tabular} & CNN & \
\begin{tabular}{c} The model achieved a recognition \\ accuracy of 91\%. \\ \end{tabular} &
\begin{tabular}{c} Lightweight networks of deep learning \\ algorithms can improve the recognition \\ accuracy and can also be implemented in \\ real-time scenarios. \\ \end{tabular} \\ \hline
[199] & \begin{tabular}{c} Motion image \\ segmentation in the \\ sport of swimming \\ \end{tabular} & GDA + SVM & \
\begin{tabular}{c} The performance of the Symmetric \\ Difference Algorithm was \\ measured in terms of recall and \\ achieved 76.2\%. \\ \end{tabular} &
\begin{tabular}{c} Using advanced optimization techniques \\ such as Cosine Annealing Schedulers with \\ deep learning algorithms may improve the \\ performance. \\ \end{tabular} \\ \hline
[200] & \begin{tabular}{c} Identifying and \\ recognizing wrong \\ strokes in table \\ tennis. \\ \end{tabular} & k-NN, SVM, Naive Bayes & \
\begin{tabular}{c} Performs various ML algorithms \\ and achieves an accuracy of 69.93\% \\ using the Naive Bayes algorithm. \\ \end{tabular} &
\begin{tabular}{c} A standard dataset can improve the accuracy \\ of recognizing the wrong strokes in table \\ tennis. \\ \end{tabular} \\ \hline
[212] & \begin{tabular}{c} Multi-player \\ tracking in sports \\ \end{tabular} & Cascade Mask R-CNN & \
\begin{tabular}{c} The proposed Deep Player \\ patterns of jersey number, team \\ class, and pose-guided partial \\ feature. To handle player identity \\ switching, the method correlates \\ the coefficients of player ID in the \\ K-shortest path with ID. The \\ proposed framework achieves \\ state-of-art performance. \\ \end{tabular} &
\begin{tabular}{c} When compared with existing methods, the \\ computation cost is higher and can be \\ considered a major drawback of the proposed \\ framework. To refine 2D detection, temporal \\ information needs to be considered and can \\ be transferred to tracking against a real-time \\ performance such as soccer, basketball, etc. \\ in object tracking. \\ \end{tabular} \\ \hline
[215] & \begin{tabular}{c} Individual player \\ tracking in sports \\ events. \\ \end{tabular} & Deep Neural Network & \
\begin{tabular}{c} Achieved an Area Under Curve \\ (AUC) of 66\% \\ \end{tabular} &
\begin{tabular}{c} Tracking by jersey number recognition may \\ increase the performance of the model. \\ \end{tabular} \\ \hline
[204] & \begin{tabular}{c} Skelton-based key \\ pose recognition \\ and classification in \\ sports \\ \end{tabular} & \begin{tabular}{c} Boltzmann machine+CNN \\ Deep Boltzmann machine \\ + RNN \\ \end{tabular} & \
\begin{tabular}{c} The proposed architecture \\ successfully analyses feature \\ extraction, motion attitude model, \\ motion detection, and behavior \\ recognition of sports postures. \\ \end{tabular} &
\begin{tabular}{c} The architecture is bound to \\ individual-oriented sports and can be further \\ implemented on group-based sports, in case \\ of challenges such as severe occlusion, \\ misdetection due to failure in blob detection \\ in object tracking. \\ \end{tabular} \\ \hline
[224] & \begin{tabular}{c} Human action \\ recognition and \\ classification in \\ sports \\ \end{tabular} & VGG 16 + RNN & \
\begin{tabular}{c} The proposed method achieved an \\ accuracy of 92\% for ten types of \\ sports \\ \end{tabular} &
\begin{tabular}{c} The model fails in the case of scaling up the \\ dataset for larger classification which shows \\ ambiguity between players and similar \\ environmental conditions. Football, Hockey; \\ Tennis, Baldmonti, Skiing, Snowboarding; \\ these pairs of classes have similar \\ environmental features; thus, it is only \\ possible to separate them based on relevant \\ actions which can be achieved by \\ state-of-the-art methods. \\ \end{tabular} \\ \hline
[237] & \begin{tabular}{c} Replay and key \\ event detection for \\ sports video \\ summarization \\ \end{tabular} & Extreme Learning & \
\begin{tabular}{c} The framework is evaluated on a \\ dataset that consists of 20 videos of four different sports categories. It \\ achieves an average accuracy of \\ 95.8\%, which illustrates the \\ significance of the method in terms \\ of key-event and replay detection \\ for video summarization. \\ \end{tabular} &
\begin{tabular}{c} The performance of the proposed method is \\ drops in the case of the absence of a gradual \\ transition of a replay segment. It can be \\ extended by incorporating artificial \\ intelligence techniques. \\ \end{tabular} \\ \hline
[228] & \begin{tabular}{c} Event detection in \\ sports videos for \\ unstructured \\ environments with \\ arbitrary camera \\ angles. \\ \end{tabular} & Mask RCNN + LSTM & \
\begin{tabular}{c} The proposed method is accurate \\ in unsupervised player extraction, \\ which is used for precise temporal \\ segmentation of rally scenes. It is \\ also robust to noise in the form of \\ camera shaking and occlusions. \\ \end{tabular} &
\begin{tabular}{c} It can be extended to doubles games with \\ fine-grained action recognition for detecting \\ various kinds of shots in an unstructured \\ video and it can be extended to analyze \\ videos of games such as cricket, soccer, etc. \\ \end{tabular} \\ \hline
[229] & \begin{tabular}{c} Human motion \\ quality assessment \\ in complex motion \\ scenarios. \\ \end{tabular} & 3-Dimensional CNN & \
\begin{tabular}{c} Achieved an accuracy of 81\% on \\ the MS-COCO dataset. \\ \end{tabular} &
\begin{tabular}{c} Instead of the Stochastic Gradient Descent \\ technique for learning rate, using the Cosine \\ annealing scheduler technique may improve \\ the performance. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}