Vision-based air-to-air multi-UAVs tracking

Vision-based air-to-air multi-object tracking is a key technology for UAV situational awareness. Recent research is limited to single-UAV tracking and migration of general multi-object tracking algorithms. To address the problem of inaccurate air-to-air multi-UAVs tracking, a cascade multi-UAVs tracking algorithm based on block feature enhancement extraction and local geometric association is designed. The UAV image is processed in blocks according to the characteristics of the fuselage and the arm, and the UAV’s fine-grained morphological features are extracted. The property that the relative geometric relationship between the UAVs is virtually invariant in the continuous frames is also utilized to extract the local geometric vectors of the UAVs. Then, the cascade association algorithm is designed by synthesizing the above technical components to improve the multi-object tracking algorithm’s ability to retrieve UAV objects and the association success rate, so as to improve the tracking performance of the proposed algorithm. Experiments show that in the test set, the proposed algorithm improves ID F1 Score (IDF1) by 5.6% compared to the state-of-the-art multi-object tracking algorithms OC-SORT, and improves Multiple Object Tracking Accuracy (MOTA) by 2.7% compared to the ByteTrack, which also performs well in the general multi-object tracking. The optimal performance for air-to-air multi-UAVs tracking can be realized, and the components used by the proposed algorithm can also be applied to SORT, BYTE and other data association algorithms to jointly improve their performance.