  We consider the problem of tracking an unknown small target from aerial
videos of medium to high altitudes. This is a challenging problem, which is
even more pronounced in unavoidable scenarios of drastic camera motion and high
density. To address this problem, we introduce a context-aware IoU-guided
tracker (COMET) that exploits a multitask two-stream network and an offline
reference proposal generation strategy. The proposed network fully exploits
target-related information by multi-scale feature learning and attention
modules. The proposed strategy introduces an efficient sampling strategy to
generalize the network on the target and its parts without imposing extra
computational complexity during online tracking. These strategies contribute
considerably in handling significant occlusions and viewpoint changes.
Empirically, COMET outperforms the state-of-the-arts in a range of aerial view
datasets that focusing on tracking small objects. Specifically, COMET
outperforms the celebrated ATOM tracker by an average margin of 6.2% (and 7%)
in precision (and success) score on challenging benchmarks of UAVDT,
VisDrone-2019, and Small-90.
