Learning a Robust Topological Relationship for Online Multiobject Tracking in UAV Scenarios

Many existing multiobject tracking (MOT) methods tend to model each object's feature individually. However, under acute viewpoint variation and occlusion, there may exist significant differences between the current and historical features of objects, which easily leads to object loss. To alleviate these issues, the topological relationships (i.e., geometric shapes formed by objects) should be modeled as a supplement to individual object features to maintain stability. In this article, we propose a novel MOT framework, which consists of a frame graph and association graph, to leverage the topological relationships both spatially and temporally. Technically, the frame graph models distance and angle among objects to resist viewpoint change, while the association graph utilizes the interframe temporal consistency of topological features to recover occluded objects. Extensive experiments on mainstream datasets demonstrate the effectiveness.