# A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios
## 3 The System and The Datasets  
### Hardware and Data Acquisition  
In order to continuously collect data, compared with the temporary equipment of the latest datasets [12; 17; 34], we built the multi-view video recording systems on four completely different fields according to different game types. This system can not only ensure that we collect high-quality and stable data, but also help reduce the difficulty of data post-processing.  
**1 + \(N\) based multi-view video recording system** There is a significant difference between our dataset and other multi-view datasets. Our approach adopts the \(1+N\) strategy, which consists of 1 main camera capable of covering the entire field, complemented by \(N\) auxiliary cameras placed around the stadium for additional coverage. Providing this main camera has the following advantages. (i). Although most existing algorithms are currently evaluated based on a single-view metric, many fail to clearly describe how this metric is calculated or from which view the evaluation should be performed. To address this issue, we proposed a standardized evaluation scale based on the main camera view, enabling direct comparison of different algorithms and reducing ambiguity between them. (ii). Algorithm designers can easily assess potential failure points and identify problems that require solutions through visualization using the main camera view. (iii). A main camera view that captures the entire field not only makes it easier to visualize data but also meets the needs of data analysis. This allows coaches and players to intuitively understand the data without frequently switching views.  
To effectively reduce the impact of occlusion on the vision algorithm, it is advisable to set up the camera at a high place. For larger fields, it becomes necessary to mount the camera higher. As a result, instead of positioning the cameras just above the average player height [17; 34], we strived to make use of the existing facilities in the field and set up cameras from different angles as high as possible. Moreover, according to the \(1+N\) strategy, we ensured that players standing in any area of the field are covered by at least two cameras, providing clearer shots from multiple camera views. The camera layout is highly overlapping as shown in Figures A1-A4. The main cameras are identified as camera-\(1\) in the figure.  
**Data acquisition.** As shown in Table 1, the dataset we constructed is not only the largest known multi-view dataset with respect to video and annotation data, but it is also the first publicly available multi-view dataset recorded for soccer player tracking during real games. We selected \(17\) groups of video clips with dense annotations from real competitions, which included varying lighting conditions and weather, and covered almost all types of situations that occur in real games. A total of \(316\) players and referees from \(34\) different teams were involved in this dataset. The details of the dataset are described in Table 2 Furthermore, we have made available to all researchers a comprehensive multi-view video dataset of several games, totaling over \(1100+\) min.  
**Synchronization.** The time synchronization difference between videos from different cameras in the same group was obtained with \(50\) microseconds (\(1/fps\)) accuracy, the accuracy of which can be observed in Figure 2.  
Based on this system, we have generated a public benchmark with high frame rate annotations, focusing on different challenging characteristics that occur during soccer scenarios. Through our data acquisition system and carefully labeled dataset, we hoped topromote the development of algorithms in this field and ultimately achieve the purpose of automatically localizing and tracking players.