上海科技大学知识管理系统

Multi-Object Tracking (MOT) holds significant potential for enhancing the analysis of sporting events. Traditional MOT models are primarily designed for pedestrian-centric scenarios with static cameras and linear motion patterns. However, the dynamic environment of sports presents unique challenges: (i) significant camera movements and dynamic focal length adjustments cause abrupt changes in player positions across frames; (ii) player trajectories are nonlinear and influenced by game dynamics, resulting in complex, rapid movements complicated by erratic camera motion; and (iii) issues like image blurring, occlusion, and similar player appearances challenge visual identification robustness. These factors create substantial obstacles for standard tracking algorithms. To address these challenges, we introduce DCTracker, a specialized MOT system for robust performance in soccer matches. Our approach enhances the conventional Kalman filter by integrating a bird's-eye view via homography and inter-frame registration for the broadcast view, termed the dual-view Kalman filter (DVKF). This method leverages context from both perspectives to enrich the estimation model with multi-state vectors for each object, mitigating the impact of camera motion and nonlinear trajectories. We also introduce the cascade selection module (CSM), which optimizes the strengths of each perspective by dynamically adjusting their influence using spatial topological relationships among players. The CSM creates an adaptive cost matrix that effectively manages visual issues from blurring and occlusion. The efficacy of our method is demonstrated through state-of-the-art performance on the SoccerNet-Tracking test set and the SportsMOT-soccer validation split, highlighting its robustness across diverse venues and challenging player trajectories.