Robust Cross-Drone Multi-Target Association Using 3D Spatial Consistency

Abstract

In this letter, we propose a robust cross-drone multi-target association algorithm that aims to associate the identities of targets detected by different drones with overlapping FOVs. Unlike other methods using image features (target appearance features and background features), our approach makes full use of the three-dimensional spatial consistency (TSC) between targets. It has wider applicability in deployed overlapping cross-drone systems with unreliable image features. Specifically, we first explore target correlation from the perspective of three-dimensional space, and propose an association method that integrates position and altitude consistency constraints, without relying on image features. After that, we mine topological mapping relationships (TMR) between cameras from the preliminary results and propose a TMR-based re-association approach using cost evaluation to optimize the preliminary results, which effectively improve association accuracy. Extensive experimental evaluations on Airsim-based dataset verify the effectiveness and robustness of our proposed method.