# 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** & **Precision and Performance Characteristics** & **Limitations and Remarks** \\ \hline
[71] & Player and ball detection and tracking in soccer. & YOLOv3 and SORT & Methodology achieved tracking accuracy of 93.7\% on multiple object tracking accuracy metrics with a detection speed of 23.7 FPS and a tracking speed of 11.3 FPS. & This methodology effectively handles challenging situations, such as partial occlusions, players and the ball reappearing after a few frames, but fails when the players are severely occluded. \\ \hline
[72] & Player, referee and ball detection and tracking by jersey color recognition in soccer. & DeepPlayerTrack & The model achieved a tracking accuracy of 96\% and 60\% on MOTA and GMOTA metrics, respectively, with a detection speed of 23 FPS. & The limitation of this method is that, when a player with the same jersey color is occluded, the ID of the player is switched. \\ \hline
[77] & Tracking soccer players to evaluate the number of goals scored by a player. & Machine Learning and Deep Reinforcement Learning. & Performance of the player tracking model measured in terms of mAP & The method failed to track the ball at critical moments such as passing at the beginning and shooting. It also failed to overcome the identity switching problem. \\ \hline
[94] & Extracting ball events to classify the player’s passing style. & Convolutional Auto-Encoder & The methodology was evaluated in terms of accuracy and achieved 76.5\% for 20 players. & Concatenation of the auto-encoder and extreme learning machine techniques will improve classification of the event performance. \\ \hline
[101] & Detecting events in soccer. & Variational Auto-encoder and EfficientNet & Achieved an F1-score of 95.2\% event images and recall of 51.2\% on images not related to soccer at a threshold value of 0.50. & The deep extreme learning machine technique which employs the auto-encoder technique may enhance the event detection accuracy. \\ \hline
[82] & Action spotting soccer video. & YOLO-like encoder & The algorithm achieved an mAP of 62.5\%. & - \\ \hline
[78] & Team performance analysis in soccer & SVM & Prediction models achieved an overall accuracy of 75.2\% in predicting the correct segmental and the outcome of the likelihood of the team making a successful attempt to score a goal on the used dataset. & The proposed model failed in identifying the players that are more frequently involved in match events that end with an attempt at scoring i.e., a “SHOT” at goal, which may assist sports analysts and team staff to develop strategies suited to an opponent’s playing style. \\ \hline
[85] & Motion Recognition of assistant referees in soccer & AlexNet, VGGNet-16, ResNet-18, and DenseNet-121 & The proposed algorithm achieved 97.56\% accuracy with real-time operations. & The proposed model failed in identifying the players that are more frequently involved in match events that end with an attempt at scoring i.e., a “SHOT” at goal, which may assist sports analysts and team staff to develop strategies suited to an opponent’s playing style. \\ \hline
[106] & Predicting the attributes (Loss or Win) in soccer. & ANN & The proposed model predicts 83.3\% for the winning case and 72.7\% for loss. & - \\ \hline
[109] & Team tactics estimation in soccer videos. & Deep Extreme Learning Machine (DELM). & The performance of the model is measured on precision, recall, and F1-score and achieved 87.6\%, 88\%, and 87.8\%, respectively. & Team tactics are estimated based on the relationship between tactics of the two teams and ball possession. The method fails to estimate the team formation at the beginning of the game. \\ \hline
[88] & Action recognition in soccer & CNN-based Gaussian Weighted event-based Action Classifier architecture & Accuracy in terms of F1-score achieved was 52.8\% for 6 classes. & By classifying the actions into subtypes, the accuracy of action recognition can be enhanced. \\ \hline
[62] & Detection and tracking of the ball in soccer videos. & VGG – MCNN & Achieved an accuracy of 87.45\%. & It could not detect when the ball moved out of play in the field, in the stands region, or from partial occlusion by players, or when ball color matched the player’s jersey. \\ \hline
[95] & Automatic event extraction for soccer videos based on multiple cameras. & YOLO & The U-encoder is designed for feature extraction and has better performance in terms of accuracy compared with fixed feature extractors. & To carry out a tactical analysis of the team, player trajectory needs to be analyzed. \\ \hline \hline \end{tabular}
\end{table}