# A Comprehensive Review of Computer Vision in Sports:
## 5 Available Datasets of Sports
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Details of the Available Dataset**} \\ \hline
**Refs.** & **Sport** & **Dataset** & **Mode of the Dataset** & **Annotated Parameters** &
\begin{tabular}{c} **Length of the Video and** \\ **Number of Images** \\ \end{tabular} \\ \hline
[101] & Soccer & \begin{tabular}{c} Image type Football \\ Keyword Dataset \\ \end{tabular} & \begin{tabular}{c} Event detection and \\ Classification. \\ \end{tabular} & \
\begin{tabular}{c} Events such as free kicks, \\ penalty kicks, tackles, red \\ cards, yellow cards. \\ \end{tabular} &
\begin{tabular}{c} Dataset was categorized as \\ training, testing, and \\ validation with 5000, 500, and \\ 500 images. \\ \end{tabular} \\ \hline
[271] & Basketball & Basketball dataset & Action Recognition & \
\begin{tabular}{c} Dribbling, Passing, \\ Shooting \\ \end{tabular} &
\begin{tabular}{c} Dataset consists of video of \\ 8 h duration, 3399 annotations \\ and 130 samples of each class. \\ \end{tabular} \\ \hline
[121] & Cricket & \begin{tabular}{c} Video type Cricket \\ Strokes Dataset \\ \end{tabular} & \begin{tabular}{c} Cricket Stroke \\ Localization. \\ \end{tabular} & \
\begin{tabular}{c} Annotated with strokes \\ played in untrimmed \\ videos. \\ \end{tabular} &
\begin{tabular}{c} Highlights dataset and the \\ Generic dataset comprised of \\ Cricket telecast videos at 25 \\ fps. \\ \end{tabular} \\ \hline
[272] & Table Tennis & \begin{tabular}{c} Video type TTNet \\ dataset \\ \end{tabular} & \begin{tabular}{c} Ball detection and \\ Event Spotting \\ \end{tabular} & \
\begin{tabular}{c} Ball bounces, Net hit, \\ Empty Events \\ \end{tabular} &
\begin{tabular}{c} 5 Videos of 10–25 min \\ duration for training and \\ 7 short videos for testing \\ \end{tabular} \\ \hline
[96,272] & Soccer & \begin{tabular}{c} Image type \\ SoccerNet and \\ SoccerNet2 dataset \\ \end{tabular} & \begin{tabular}{c} Action spotting in \\ soccer videos \\ \end{tabular} & \
\begin{tabular}{c} Goal, Yellow/Red Card, \\ Substitution \\ \end{tabular} &
\begin{tabular}{c} Handles a length of video of \\ about 764 h and \\ 6637 moments which are split \\ into three major classes \\ (Substitution, Goal, and \\ Yellow/Red Card). \\ \end{tabular} \\ \hline
[165] & Volleyball & Volleyball dataset & \
\begin{tabular}{c} Group activity \\ recognition \\ \end{tabular} &
\begin{tabular}{c} Person-level actions, \\ Temporal dynamics of a \\ person’s action and \\ Temporal evolution of \\ group activity \\ \end{tabular} & 1525 frames were annotated. \\ \hline
[273] & Hockey & \begin{tabular}{c} The Spectators of \\ Hockey (S-HOCK) \\ \end{tabular} & \begin{tabular}{c} Analyzing Crowds at \\ the Stadium \\ \end{tabular} & \
\begin{tabular}{c} spectator categorization \\ such as position, head pose, \\ posture and action \\ \end{tabular} &
\begin{tabular}{c} Video type dataset. \\ 31 s \\ 30 fps \\ \end{tabular} \\ \hline
[281] & \begin{tabular}{c} 487 classes of \\ sports \\ \end{tabular} & Sports-1M dataset & \begin{tabular}{c} Sports classification \\ and activity \\ recognition. \\ \end{tabular} & \
\begin{tabular}{c} Activity labels \\ \end{tabular} &
\begin{tabular}{c} 5 m 36 s \\ \end{tabular} \\ \hline
[276] & Soccer & \begin{tabular}{c} Player position in \\ soccer video dataset \\ \end{tabular} & \
\begin{tabular}{c} Player tracking \\ system \\ \end{tabular} &
\begin{tabular}{c} Trajectories of players \\ \end{tabular} & 45 min \\ \hline
[225] & Volleyball & \begin{tabular}{c} Indoor volleyball \\ dataset \\ \end{tabular} & \begin{tabular}{c} Activity Detection \\ and Recognition \\ \end{tabular} & \
\begin{tabular}{c} Seven activities such as the \\ stereo, reception, setting, \\ attack, block, stand, and \\ defense/move are \\ annotated to each player in \\ this dataset. \\ \end{tabular} &
\begin{tabular}{c} Video type dataset. \\ It contains 16 sports classes, \\ with 50 sequences per class. \\ \end{tabular} \\ \hline
[274] & \begin{tabular}{c} Various jump \\ games, various \\ throw games, \\ bowling, tennis \\ serving, diving, \\ and weightlifting. \\ \end{tabular} & \begin{tabular}{c} Olympic Sports \\ Dataset \\ activities in sports \\ \end{tabular} & \begin{tabular}{c} Recognition of \\ complex human \\ activities in sports \\ \end{tabular} & \
\begin{tabular}{c} Different poses in different \\ sports \\ \end{tabular} &
\begin{tabular}{c} Video type dataset. \\ It contains 16 sports classes, \\ with 50 sequences per class. \\ \end{tabular} \\ \hline
[34] & Basketball & NCAA Dataset & Event Recognition & \
\begin{tabular}{c} Event classification, Event \\ detection and Evaluation of \\ attention. \\ \end{tabular} &
\begin{tabular}{c} The video type dataset. \\ Length 1.5 h long \\ Annotated with 11 types of \\ events. \\ \end{tabular} \\ \hline
[277] & Soccer & ISSIA Soccer Dataset & \
\begin{tabular}{c} Objective method of \\ Ground Truth \\ Generation \\ \end{tabular} & Player and Ball trajectories &
\begin{tabular}{c} 2 min \\ 25 fps \\ \end{tabular} \\ \hline
[280] & Basketball & \begin{tabular}{c} APIDIS Basketball \\ Dataset \\ \end{tabular} & \begin{tabular}{c} sport-event \\ summarization \\ \end{tabular} & \
\begin{tabular}{c} Basketball events such as \\ the position of players, \\ referees, and the ball. \\ \end{tabular} &
\begin{tabular}{c} 16 min, \\ 22 fps \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}