# A Comprehensive Review of Computer Vision in Sports:
## 4 Literature Review
### Hockey/Ice Hockey
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & & & \\ \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Problem Statement**} & \multirow{2}{*}{**Proposed Methodology**} & \multirow{2}{*}{**Precision and Performance Characteristics**} & \multirow{2}{*}{**Limitations and Remarks**} \\ \cline{1-1}  & & & & & HD+SVM achieved the best results in terms of accuracy, recall, and F1-score with values of 77.24\%, 69.23\%, and 73.02\%. & The model failed to detect the players in occlusion conditions. \\ \hline \multirow{2}{*}{[188]} & \multirow{2}{*}{Localizing puck Position and Event recognition.} & \multirow{2}{*}{Faster RCNN} & \multirow{2}{*}{Evaluated in terms of AUC and achieved 73.1\%.} & Replacing the detection method with the YOLO series can improve the performance. \\ \hline \multirow{2}{*}{[181]} & \multirow{2}{*}{Identification of players in hockey.} & \multirow{2}{*}{ResNet + LSTM} & Achieves player identification accuracy of over 87\% on the split dataset. & Some of the jersey number classes such as 1 to 4 are incorrectly predicted. The diagonal numbers from 1 to 100 are falsely classified due to the small number of training examples. \\ \hline \multirow{2}{*}{[177]} & \multirow{2}{*}{Activity recognition in a hockey game.} & \multirow{2}{*}{LSTM} & The proposed model recognizes the activities such as free hits, goals, penalties corners, and long corners with an accuracy of 98\%. & As the proposed model is focused on spatial features, it does not recognize activities such as free hits and long corners as they appear as similar patterns. By including temporal features and incorporating LSTM into the model, the model is robust to performance accuracy. \\ \hline \multirow{2}{*}{[180]} & Pose estimation and temporal-based action recognition in hockey. & \multirow{2}{*}{VGG19 + LiteFlowNet + CNN} & A novel approach was designed and achieved an accuracy of 85\% for action recognition. & The architecture is not robust to abrupt changes in the video, e.g., it fails to predict hockey sticks. Activities such as a goal being scored, or puck location, are not recognized. \\ \hline \multirow{2}{*}{[187]} & Action recognition in ice hockey using a player pose sequence. & \multirow{2}{*}{CNN+LSTM} & The performance of the model is better in similar classes such as passing and shooting. It achieved 90\% parameter reduction and 80\% floating-point reduction on the HARPET dataset & As the number of hidden units to LSTM increases, the number of parameters also increases, which leads to overfitting and low test accuracy. \\ \hline \multirow{2}{*}{[178]} & Human activity recognition in hockey. & \multirow{2}{*}{CNN+LSTM} & An F1-score of 67\% was calculated for action recognition on the multi-labeled imbalanced dataset. & The performance of the model is poor because of the improper imbalanced dataset. \\ \hline \multirow{2}{*}{[179]} & Player action recognition in an ice hockey game & \multirow{2}{*}{CNN} & The accuracy of the actions recognized in a hockey game is 65\% and when similar actions are merged accuracy rises to 78\%. & Pose estimation problems due to severe occlusions when motions blur due to the speed of the game and also due to lack of a proper dataset to train models, all causing low accuracy. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Studies in hockey.