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-player action recognition for automatic analysis in sports is the subject of increasing attention. Trajectory-tracking technology is key for accurate recognition, but little research has focused on this aspect, especially for non-professional matches. Here, we study multi-player tracking in the most popular and complex sport among non-professionals—soccer. In this non-professional soccer player tracking (NPSPT) challenge, single-view-based motion recording systems for continuous data collection were installed in several soccer fields, and a new benchmark dataset was collected. The dataset consists of 17 2-min long super-high-resolution videos with diverse game types consistently labeled across time, covering almost all possible situations for multi-player detection and tracking in real games. A comprehensive evaluation was conducted on the state-of-the-art multi-object-Tracking (MOT) systems, revealing insights into player tracking in real games. Our challenge introduces a new dimension for researchers in the player recognition field and will be beneficial to further studies.

Localization and tracking in multi-player sports present significant challenges. Existing GPS and UWB-based methods have higher accuracy but require players to wear inconvenient electronic devices. On the other hand, camera-based solutions are device-free, but have unsatisfied accuracy and necessitate significant manual intervention. Thus, we introduced a LiDAR-Camera fused system tailored for soccer scenarios, that can combine highest position accuracy and convenient application. With this continuous data collection system, a new benchmark dataset is constructed, and a simple and effective tracking-by-detection method is designed. The method comprises two main module. The Cascade PointNet module effectively identifies players from the point cloud, while the subsequent Tracklet-Zoom module efficiently performs tracklet association. Our method shows significant improvements in multiple evaluation metrics compared to other methods. Specifically, we achieved a score of 95.21% on the GT-consistency metric, representing a 13.29% increase. This indicates that our method has made substantial progress towards achieving automated tracking.