# A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios
## 6 Conclusions  
The advancement of multi-view multi-object tracking methods for sports scenes is hindered by the limited availability of suitable datasets. To address this issue, we have developed multi-view recording systems and introduced a new dataset with high-quality and dense annotations. Our approach provides researchers with a rich and diverse set of data, allowing them to conduct more accurate and detailed analyses of the soccer scenarios. By leveraging our new dataset and recording systems, researchers can gain deeper insights into the behaviors and interactions of the players, ultimately leading to more advanced and impactful research in a range of fields. Despite significant advances in computer vision and machine learning, our experiments revealed that the accuracy of four leading methods is not optimal, indicating that tracking in soccer and sports poses significant challenges to the research community. Addressing these challenges could have a significant impact on various areas, by enabling more precise and informative insights into player tracking. Furthermore, the dataset is distinguished by a considerable volume of unlabeled video clips, in addition to a substantial amount of annotated data. This unique feature of the dataset makes it highly valuable for unsupervised learning techniques, as it presents the opportunity to evaluate the performance of such models on both the labeled and unlabeled portions of the dataset, enabling the potential discovery of new patterns and insights in the data. To further automate game analysis, we recognize the limitations and challenges of our current system, which relies on commercial surveillance cameras. When strong backlighting occurs, it becomes difficult to obtain clear features of athletes. Additionally, the cameras are mounted at a high height and cover a large area, which makes it impossible to accurately recognize the players' postures and the movements of the football. To address these limitations, we plan to incorporate more optical sensors such as Lidar and UWB sensors into our recording system. This will allow us to obtain more comprehensive and in-depth information about the game. Additionally, we will release more labeled data that captures \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**GameType** & **Algorithms** & **HOTA \(\uparrow\)** & **DetA \(\uparrow\)** & **MOTA \(\uparrow\)** & **IDF1 \(\uparrow\)** & **IDSW \(\downarrow\)** \\ \hline
5-A-Side & TRACTA & 57.42 & 72.77 & 90.12 & 70.30 & 155 \\  & MvMHAT & 58.87 & 71.65 & 91.27 & 71.97 & 143 \\  & DAN4 Ass & 60.75 & 73.38 & 92.68 & 74.28 & 137 \\  & MVFlow & 62.52 & 75.95 & 93.08 & 74.11 & 124 \\ \hline
7-A-Side & TRACTA & 70.33 & 80.89 & 94.27 & 80.41 & 79 \\  & MvMHAT & 75.91 & 82.55 & 95.02 & 82.50 & 70 \\  & DAN4 Ass & 77.52 & 82.29 & 95.26 & 83.97 & 51 \\  & MVFlow & 76.82 & 81.89 & 94.83 & 82.89 & 46 \\ \hline
8-A-Side & TRACTA & 67.33 & 80.26 & 90.99 & 77.71 & 101 \\  & MvMHAT & 68.80 & 80.98 & 91.57 & 80.75 & 89 \\  & DAN4 Ass & 70.22 & 85.75 & 93.50 & 81.87 & 80 \\  & MVFlow & 72.57 & 87.31 & 94.89 & 83.75 & 76 \\ \hline
11-A-Side & TRACTA & 60.75 & 79.59 & 81.89 & 62.33 & 111 \\  & MvMHAT & 60.62 & 80.11 & 80.25 & 64.35 & 107 \\  & DAN4 Ass & 63.27 & 82.86 & 79.75 & 63.71 & 102 \\  & MVFlow & 64.89 & 83.77 & 85.73 & 69.92 & 93 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the candidate methods on our dataset. the combination of human and ball movement. This richer and deeper dataset will enable machine learning algorithms to better understand the complexities of game situations. As we continue to develop our system, we remain open to feedback from the research community regarding its effectiveness and limitations. We believe that this feedback will be crucial in helping us improve the quality of our data and the accuracy of our automated analysis. Ultimately, our goal is to create a more accurate and reliable system for automated game analysis that can benefit players, coaches, and researchers. [PERSON] and [PERSON] proposed the idea of the paper.