## 1 Introduction  
A player's running trajectory data is the fundamental element for both action recognition and data analysis [1, 2, 3]. For example, in soccer, data analysts can calculate basic data (such as the dirbbling distance and the movement speed distribution) to evaluate a single player's athletic ability, and combine the dynamic positions of all players to analyze the team's offensive and defensive formation strategies at the tactical level [4]. Data analysts can even combine the player's movement and on-ball actions (such as passing and receiving) to obtain a deeper technical and tactical analysis [5]. The current player trajectory tracking systems often require players to wear motion sensors, or multiple high-resolution cameras and information sensors to be installed around the stadium [6]. These solutions often cost millions of dollars, and consequently are only available to high-level professional clubs and top competitions [7]. With the rapid development of machine learning and video recording technology, it has become possible to establish a low-cost and high-volume player performance analysis system, based on a single camera, to help non-professional players and teams obtain their own running trajectories. In fact, multi-player-tracking can be considered as a subtask of a multiple-object-tracking (MOT) problem [8], a crucial task in computer vision, which aims to extract trajectories of numerous moving objects in an image sequence. We propose a new challenge focusing on how to apply the MOT methods into the seldom investigated scenarios of multi-player sports, and obtain running data automatically for non-professionals. Soccer is the most representative and complicated multi-player sport. If an MOT algorithm can be successfully implemented in soccer, it can be implemented in many other sports. Thus, we propose the NPSPT (non-professional-soccer-player-tracking) challenge. Unlike conventional MOT problems, which focus more on pedestrians walking on sidewalks [9, 10] or vehicles traveling on roads [11], players running on a soccer field exhibit much variation:  
1. What this challenge needs to accomplish is to consistently track the player's trajectory throughout the game. The duration is generally short in existing datasets, and the target can last an even shorter time. 2. Most datasets focus on a narrow area from a mid-to-low perspective for video recording. Compared to these, the movement range of the players in this challenge covers the entire field, which requires the camera to completely record the field from a bird's-eye view (from 5-a-side 800 m\({}^{2}\) to 11-a-side 6936 m\({}^{2}\), see Figure 1). Therefore, the proportion of the target in the image is much smaller than that of the existing dataset. 3. Compared to slow-moving objects in existing datasets, players perform a lot of sprinting and changing of direction on the field. 4. Due to their identical jerseys, players on the same team are prone to be confused with each other when they overlap. Existing datasets also lack similar situations. After considering the deficiencies of the current datasets, we installed single-view based recording systems in several soccer fields for a real-time and automatic processing challenge, and introduced a new benchmark dataset--**NPSPT** (non-professional soccer player tracking) for training and testing the MOT algorithms. Our dataset features 17 super-high-resolution videos (equal or larger to 2816 \(\times\) 1408) with more than 40,800 frames of real soccer games recorded by our systems. These video clips are manually annotated with a total of 736,800 labelled bounding boxes of 310+ different players and referees, which are 2 min long on average, covering all situations mentioned above. Table 1 shows a detailed comparison to other sports datasets. The region-limitation parameters are provided to exclude the audience from the targets. The real world coordinates data are also provided for all of those sequences to enable real-world coordinate tracking.