Anti-Occlusion Cross-UAV Target Association Using Spatiotemporal Consistency

In this article, we propose an anti-occlusion target association algorithm in an overlapping cross-uncrewed aerial vehicle (UAV) system. This method focuses on associating targets with the same ID detected by different UAVs. Unlike other approaches that rely solely on images captured by optical cameras, our approach fully leverages data from multiple sensors to extract more reliable and stable association features. Therefore, it still maintains robustness in the face of common challenges, such as unreliable image features and occluded targets. First, we introduced a novel similarity measurement model based on spatial consistency (SC), developed through an analysis of altitude and positional relationships among targets detected by multiple UAVs. This approach leverages data fusion from multiple sensors, including cameras, IMUs, and GPS. Simultaneously, we use a tracker to directly transmit association relationships from previous frames based on temporal consistency (TC) and design a decision module to adaptively select a more robust SC-based strategy or a more efficient TC-based strategy. This significantly improves the efficiency of the algorithm without compromising accuracy. After that, the homography constraint is utilized to optimize the preliminary results and restore the occluded targets. To achieve this, we propose a cost evaluation method to enhance the effectiveness of optimization and restoration by filtering out unreliable homography matrices. Extensive experiments verify the effectiveness and robustness of our proposed method.