  Efficiency has been a critical problem in UAV tracking due to limitations in
computation resources, battery capacity, and unmanned aerial vehicle maximum
load. Although discriminative correlation filters (DCF)-based trackers prevail
in this field for their favorable efficiency, some recently proposed
lightweight deep learning (DL)-based trackers using model compression
demonstrated quite remarkable CPU efficiency as well as precision.
Unfortunately, the model compression methods utilized by these works, though
simple, are still unable to achieve satisfying tracking precision with higher
compression rates. This paper aims to exploit disentangled representation
learning with mutual information maximization (DR-MIM) to further improve
DL-based trackers' precision and efficiency for UAV tracking. The proposed
disentangled representation separates the feature into an identity-related and
an identity-unrelated features. Only the latter is used, which enhances the
effectiveness of the feature representation for subsequent classification and
regression tasks. Extensive experiments on four UAV benchmarks, including
UAV123@10fps, DTB70, UAVDT and VisDrone2018, show that our DR-MIM tracker
significantly outperforms state-of-the-art UAV tracking methods.
