# A Comprehensive Review of Computer Vision in Sports:
## 4 Literature Review
### Basketball  
Basketball is a sport played between two teams consisting of five players each. The task of this sport is to score more points than the opponent. This sport has several activities with the ball such as passing, throwing, bouncing, batting, or rolling the ball from one player to another. Physical contact with an opponent player may be a foul if the contact impedes the players' desired movement. The advancements in computer vision techniques have effectively employed fully automated systems to replace the manual analysis of basketball sports. Recognizing the player's action and classifying the events [29; 30; 31] in basketball videos helps to analyze the player's performance. Player/ball detection and tracking in basketball videos are carried out in [32; 33; 34; 35; 36; 37] but fail in assigning specific identification to avoid identity switching among the players when they cross. By estimating the pose of the player, the trajectory of the ball [38; 39] is estimated from various distances to the basket. By recognizing and classifying the referee's signals [40], player behavior can be assessed and highlights of the game can be extracted [41]. The behavior of a basketball team [42] can be characterized by the dynamics of space creation presented in [43; 44; 45; 46; 47; 48] that works to counteract space creation dynamics with a defensive play presented in [49]. By detecting the specific location of the player and ball in the basketball court, the player movement can be predicted [50] and the ball trajectory [51; 52; 53] can be generated in three dimensions which is a complicated task. It is also necessary to study the extraction of basketball players' shooting motion trajectory, combined with the image feature analysis method of basketball shooting, to reconstruct and quantitatively track the basketball players' shooting motion trajectory [54; 55; 56; 57]. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data is complicated. Therefore, it is necessary to analyze the real-time gameplay [58]. Table 2 summarizes various proposed methodologies used to complete various challenging tasks in basketball sport including their limitations. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Problem Statement**} & \multirow{2}{*}{**Proposed Methodology**} & \multicolumn{2}{c}{**Precision and Performance Characteristics**} & \multirow{2}{*}{**Limitations and Remarks**} \\ \hline \multirow{4}{*}{[31]} & Recognizing actions of & & The metrics used to evaluate the \\  & basketball players by & & method are the Spearman rank-order \\  & using image & & correlation coefficient, Kendall \\  & & & rank-order correlation coefficient, \\  & recognition techniques & & Sequence2 Sequence & Pearson linear correlation coefficient, \\  & recognition techniques & & and Root Mean Squared Error and \\  & & & achieved 0.921, 0.803, 0.932, and 1.03, \\  & & & respectively. \\ \hline \multirow{4}{*}{[54]} & Multi-future trajectory & & Conditional Variational \\  & prediction in & & Recurrent Neural \\  & basketball. & & Networks \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{The proposed methodology fails to predict the trajectories in the case of uncertain and complex scenarios. As the behavior of the basketball or players is dynamic, belief maps cannot steer future positions. Training the model with a dataset of different events can rectify the failures of predictions.} \\ \end{tabular}
\end{table} 
Table 2: Studies in basketball.