## 3 System Construction
### Data Generation  
Most existing video datasets focused on one or a few operating domains. For instance, MOTChallenge benchmark [9; 36] corresponds to urban, outdoor scenes, featuring crowds of people. As a result, methods developed on this dataset (and hence domain) are hard to generalize in the soccer scenes.  
Real-games-challengesWe constructed NPSPT by selecting clips from a variety of real game videos, covering most confusing types of situations that happen in the real games. The situations include: crowded players in the penalty area when a set kick (corner kick or free kick) was taken. Players hugging and celebrating after a goal. The target player briefly leaving the target area during a throw-in; a new tracking target emerging when a substitution is made. Players running rapidly when a defensive counterattack occurs, and players constantly crossing and changing positions when defending a position.  
A total of 17 real competition clips were selected from different competitions in sunny weather and good lighting conditions, covering over 310 players and referees from 34 different teams.  
Video QualityAccording to the statistics of the round analysis method, a complete round in a soccer game is usually less than 1 min. Each of our clips is as long as two minutes, which captures the real game situations.  
Region LimitationWhile covering the entire field, the wide-angle camera will also include substitutes and audience around the field, but they are not effective targets. Therefore, it is necessary to use an effective region limitation method to distinguish the players inside the field from other people. [PERSON] et al. [47] used the manually outlined binary map to limit the target region. Although this method can effectively eliminate the interference of people outside the field, the effective information of the target player or referee near the edge of the field will also be erased incidentally. According to the characteristics of the camera distortion, we used the quadratic curve equations to fit each straight edge of the court in the distorted image separately. \((x_{bc},y_{bc})\) is the bottom center of target bounding box, \([a,b,c,a]\) are the parameters of quadratic curve equation. The targets that satisfy Equation (1) will be considered as valid ones. Finally, the region enclosed by multiple quadratic curves can more accurately define the scope of the target region without losing information. Figure 2 shows the comparison between the two methods.  
\[(a_{i}\times x_{bc}^{2}+b_{i}\times x_{bc}+c_{i}-y_{bc})\times\alpha_{i}\geq 0 \quad\forall\;i\in\{1,2,3,4\} \tag{1}\]  
**Real World Coordinates.** In addition, we established a map between the camera view and the real physical plane. After calibrating the cameras [48], the pixel coordinates of the bottom center of the bounding-boxes can be converted to world coordinates (the value of the z-axis is 0 by default) It will help data analysts to get the real movement trajectories of all players and do more accurate analysis at the physical level. Details are shown in Figure 3. World coordinates and calibration parameters for each view were provided.  
This is the first dataset of our work and there are several aspects to improve. First of all, the current amount of our dataset is not large enough, and it does not include certain special cases (suspension and entry of off-site personnel). Secondly, all the clips were recorded during sunny day in good light. Other weather conditions are not included.