# A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios
###### Abstract  
Localization and tracking in multi-player sports present significant challenges, particularly in wide and crowded scenes where severe occlusions can occur. Traditional solutions relying on a single camera are limited in their ability to accurately identify players and may result in ambiguous detection. To overcome these challenges, we proposed fusing information from multiple cameras positioned around the field to improve positioning accuracy and eliminate occlusion effects. Specifically, we focused on soccer, a popular and representative multi-player sport, and developed a multi-view recording system based on a \(1+N\) strategy. This system enabled us to construct a new benchmark dataset and continuously collect data from several sports fields. The dataset includes 17 sets of densely annotated multi-view videos, each lasting 2 min, as well as 1100+ min multi-view videos. It encompasses a wide range of game types and nearly all scenarios that could arise during real game tracking. Finally, we conducted a thorough assessment of four multi-view multi-object tracking (MVMOT) methods and gained valuable insights into the tracking process in actual games. multi-view;tracking; soccer; system; benchmark; +
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the complete trajectories from multi-view video streams. Using joint visual information from multiple synchronized cameras [28; 29; 30] will provide reliable estimates in crowded scenes. Although multi-view multi-object tracking has flourished in the past few years, there is currently no large-scale and high-quality public dataset that focuses on tracking players on the field. The most commonly used MVMOT standard datasets [31; 32; 33; 34; 35] all target pedestrians in limited scenes. The most similar dataset LH0 [17], recorded junior players running by scripts on a 9 m \(\times\) 26 m square, with annotations that are limited, making it difficult to train and test algorithms. Additionally, the cameras are positioned only slightly above the average human height, meaning that throughout the sequence, occluded situations are very difficult to deal with. The lack of relevant datasets makes it impossible to verify whether the existing methods can solve the problems in sports scenarios. To accelerate the sports analysis research, we focused our attention on soccer, the most challenging and the most popular multi-player sport. More specifically, we installed the multi-view video recording system at several soccer fields for continuous data collection. The system was designed under the \(1+N\) strategy, which consists of 5\(\sim\)7 static super-high-resolution cameras with overlapping Fields of View (FOV). Then we created a large-scale player tracking dataset capturing unscripted games in various soccer scenarios on these fields. The dataset comprises three parts: 17 groups of time-synchronized 2 min long videos from multiple cameras with 20 fps tracking annotation for each player, precise joint camera calibrations that include intrinsic and extrinsic parameters, and a total of 1100\(+\) min of multi-view videos. Our experiments have demonstrated that trackers designed using publicly available datasets perform poorly in challenging sports environments due to differences in viewpoints and domain gaps. We anticipate that the release of this multi-view multi-player tracking dataset will inspire further research in this field. Additionally, the dataset holds substantial value for related tasks such as multi-view human detection and tracking multiple individuals with a single camera. To summarize, our workflow proceeded as shown in this Figure 1, and our contributions are as follows:  
1. We designed and installed \(1+N\) based multi-view video recording systems in four soccer fields for continuous data collection. 2. We designed a novel \(1+N\) MVMOT annotation strategy based on the systems. 3.