Localization and tracking in multiplayer sports present significant challenges. Existing global positioning system (GPS) and ultra wideband (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 light detection and ranging (LiDAR)-camera fused system tailored for soccer scenarios, which 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 modules. 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 with 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 toward achieving automated tracking.