FusionTrack: Collaborative Multi-Object Tracking with Arbitrary Multi-UAVs

Multi-object tracking (MOT) via UAV fleets is crucial for low-altitudeapplications. However, existing multi-view multi-object tracking (MVMOT) meth-ods and datasets are constrained by fixed camera configurations and limited cov-erage of complex scenarios, thereby failing to address real-world applications. Tofill this gap, we first conduct a systematic study of MOT under arbitrary camerasetups and introduce MDMOT, a novel multi-UAV benchmark covering diversereal-world scenarios, exposing practical challenges like random object entry/exitand cross-view appearance inconsistencies. We further propose FusionTrack, anend-to-end MVMOT framework that abandons the traditional decoupled track-then-associate paradigm. It jointly optimizes tracking and association via bidirec-tional object fusion between a Tracklet Memory Pool and a Trajectory IdentityPool, leveraging spatio-temporal context to enhance representation discriminabil-ity. For inference, we design View-aware Hierarchical Clustering with neighborfiltering to ensure intra-view exclusivity and inter-view consistency in cross-viewassociation. Extensive experiments verify that FusionTrack achieves state-of-the-art performance in both single- and multi-view tracking tasks, outperforming ex-isting methods by at least 2.3% in relative improvement on our MDMOT bench-mark. Project page: https://github.com/aircas501/FusionTrack