# A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios
## 2 Related Works
### Datasets
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Dataset** & **Resolution** & **Cameras** & **FPS** & **IDs** & **Annotations** & **Size/Duration** & **Scenarios** & **Field Size** \\ \hline
**KITTI [12]** & 1392 \(\times\) 480 & 4 & 10 & 10 & 389 \({}^{\ddagger}\) & 7 min & campus roads & \\
**Laboratory [21]** & 320 \(\times\) 240 & 4 & 25 & 6 & 264 (1 fps) & 4.4 min & laboratory \({}^{\ddagger}\) & 5.5 \(\times\) 5.5 m\({}^{2}\) \\
**Terrace [21]** & 320 \(\times\) 240 & 4 & 25 & 9 & 1023 (1 fps) & 3.5 min & terrace \({}^{\ddagger}\) & 10 \(\times\) 10 m\({}^{2}\) \\
**Passageway [21]** & 320 \(\times\) 240 & 4 & 25 & 13 & 226 (1 fps) & 20 min & square \({}^{\ddagger}\) & 10 \(\times\) 6 m\({}^{2}\) \\
**Campus [32]** & 1920 \(\times\) 1080 & 4 & 30 & 25 & 240 (1 fps) & 16 min & gargen \({}^{\ddagger}\) & \\
**CMC \({}^{\star}\)**[31]** & 1920 \(\times\) 1080 & 4 & 4 & 15 & 11,719 (4 fps) & 1494 frames & laboratory \({}^{\ddagger}\) & 7.67 \(\times\) 3.41 m\({}^{2}\) \\
**SAISA [33]** & 1024 \(\times\) 768 & 4 & 15 & 18 & 1200 (0.3 fps) & 60 min & lobby \({}^{\ddagger}\) & \\
**EPFL-RLC [35]** & 1920 \(\times\) 1080 & 3 & 60 & - & 6132 \({}^{\ddagger}\) & 8000 frames & lobby & \\
**PETS-2009 [36]** & 768 \(\times\) 576 & 7 & 7 & 19 & 4650 (7 fps) & 795 frames & campus roads \({}^{\ddagger}\) & \\
**WildTrack [34]** & 1920 \(\times\) 1080 & 7 & 60 & 313 & 66,626 (2 fps) & 60 min & square & 36 \(\times\) 12 m\({}^{2}\) \\
**APIDIS [37]** & 1600 \(\times\) 1200 & 7 & 22 & 12 & 86,870 (25 fps) & 1 min & Basketball Count \({}^{\ddagger}\) & 28 \(\times\) 15 m\({}^{2}\) \\
**LI0 \({}^{\ddagger}\)**[17]** & 1920 \(\times\) 1080 & 8 & 25 & 26 & 26,000 (2 fps) & 1.3 min & soccer field \({}^{\ddagger}\) & 38 \(\times\) 7 m\({}^{2}\) \\  & & & & & & & 40 \(\times\) 20 m\({}^{2}\) & 40 \(\times\) 20 m\({}^{2}\) \\  & & 3840 \(\times\) 2160 & 5 & & 727,179 (20 fps) & & & 40 \(\times\) 20 m\({}^{2}\) \\
**Ours** & 4736 \(\times\) 1400 & 5 & & +137,846 (1 fps) & & & & 68.3 \(\times\) 48.5 m\({}^{2}\) \\  & & 5950 \(\times\) 1152 & 7 & & & & & 101.8 \(\times\) 68.5 m\({}^{2}\) \\ \hline \hline \end{tabular}  
\({}^{\dagger}\) The collection of the dataset is performed by the script. \(\ddagger\) The dataset only provides annotations for detection.  
\({}^{\star}\) The dataset is not open to all researchers.  
\end{table} 
Table 1: Public Multi-view Datasets. APIDIS [37] and LH0 [17] are among the few datasets that focus on sports scenes, and are of particular interest to us. These datasets were created by recording short clips of basketball and soccer training, respectively, using seven and eight static cameras. While the duration of these clips is very short, and they are not formal game videos, they still provide valuable information for the application of multi-view algorithms in sports scenes. Due to the requirement for total automation, long-term stability, and precise player tracking, the models developed using the aforementioned datasets are insufficient. It is crucial to create a specialized dataset tailored to these specific needs.