Resumen
Multi-UAV cooperative systems are highly regarded in the field of cooperative multi-target localization and tracking due to their advantages of wide coverage and multi-dimensional perception. However, due to the similarity of target visual characteristics and the limitation of UAV sensor resolution, it is difficult for UAVs to correctly distinguish targets that are visually similar to their associations. Incorrect correlation matching between targets will result in incorrect localization and tracking of multiple targets by multiple UAVs. In order to solve the association problem of targets with similar visual characteristics and reduce the localization and tracking errors caused by target association errors, based on the relative positions of the targets, the paper proposes a globally consistent target association algorithm for multiple UAV vision sensors based on triangular topological sequences. In contrast to Siamese neural networks and trajectory correlation, the relative position relationship between targets is used to distinguish and correlate targets with similar visual features and trajectories. The sequence of neighboring triangles of targets is constructed using the relative position relationship, and the feature is a specific triangular network. Moreover, a method for calculating topological sequence similarity with similar transformation invariance is proposed, as well as a two-step optimal association method that considers global objective association consistency. The results of flight experiments indicate that the algorithm achieves an association accuracy of 84.63%, and that two-step association is 12.83% more accurate than single-step association. Through this work, the multi-target association problem with similar or even identical visual characteristics can be solved in the task of cooperative surveillance and tracking of suspicious vehicles on the ground by multiple UAVs.