Group Target Fine Tracking Algorithm Based on Velocity Correction Via Collaborative Relationships

In scenarios where fine-grained tracking of individual targets within a group is required, dense groups often lead to association errors, resulting in false or unstable tracks. To address this issue, this paper proposes a fine-grained group target tracking algorithm based on velocity correction via group collaboration relationships. By analyzing the overall motion characteristics of the group and applying velocity correction to individual targets, the algorithm effectively reduces false and unstable tracks within the group. Simulation experiments demonstrate that the proposed method significantly decreases erroneous tracks and improves tracking accuracy in fine-grained group target tracking applications.