# A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios
## 3 The System and The Datasets
### Statistics  
**Annotated Frames and Videos.** Our dataset, as shown in Table 1, is not only the closest to soccer scenarios, but also provides the largest amount of annotations. We have completed annotations for a total of 40,800 frames of data, with 2040 frames providing complete annotations from all camera views. For all main camera views, we have completed 727,179 identity annotations, and for all auxiliary camera views, we have completed a total of 137,846 identity annotations. These 865,025 annotations are an order of magnitude larger than the next largest dataset [34; 37]. Additionally, a significant proportion of the videos are  
Figure 4: GUI of our self-designed multi-camera annotation software. The software allows annotators to quickly assign corresponding identities and adjust the size of detection predictions in auxiliary cameras by connecting them to bounding boxes in camera-1. (1) The bounding boxes in camera-1 are based on manual annotations, whereas the bounding boxes in other cameras are generated by the detector. (2) The purple lines serve to demarcate the target area [9]. (3) The blue bounding box has been adjusted to the correct size and aligned with the identities across multiple views. (4) The yellow bounding boxes are being adjusted and aligned. (5) The red bounding boxes are waiting to be adjusted and aligned. (6) The number above the bounding box indicates its identity. (7) The software utilizes calibration parameters to approximately locate the detection predictions in auxiliary camera views.  
unlabeled, and we will publicly release complete multi-view game videos that total over 1100 min. This will be of significant value for unsupervised methods and can serve as a benchmark for the annotated portion.  
**Field Size and Resolution.** Multi-player sports, especially soccer, have high field size requirements. Table 2 shows that the size of the fields involved in our dataset ranges from 40 \(\times\) 20 m\({}^{2}\) to 101.8 \(\times\) 68.5 m\({}^{2}\), and the area where the players are active is much larger than other datasets. To capture sufficient detail, we used zoomable ultra-high-resolution cameras in all fields.  
**Track Length and Motion Speed.** In Figure 5, we present a detailed analysis of our dataset and compare it to the WildTrack dataset [34], which is the standard dataset used for evaluating most state-of-the-art models.  
In Figure 5a, we present the distribution of object track lengths, while Figure 5b shows the distribution of motion speed. Due to WildTrack's annotation frame rate of only 1 fps, we increased the data amount by interpolation. In WildTrack, the target length is generally shorter, and the motion speed is slower, mainly due to most pedestrians walking directly through the square, with only a few people standing still and chatting, making them relatively easier to track. In contrast, our dataset focuses on players on the field who move constantly throughout the clip, with more running and sprinting. Consequently, our dataset provides longer track lengths and faster speeds for the players, requiring tracking methods that can stably track all players for an extended duration.  
**Data split.** Our dataset was designed as an evaluation benchmark, so we divided it into two subsets--train and test. The training subset consists of 13 videos, while the testing subset contains 4 videos. This division was performed to ensure that the videos in each subset are distinct from each other. The trackers were trained using the videos in the training subset and then tested using the corresponding videos in the testing subset. For more information on how the subsets were constructed, refer to Column 9 of Table 2.