  RGB-D tracking significantly improves the accuracy of object tracking.
However, its dependency on real depth inputs and the complexity involved in
multi-modal fusion limit its applicability across various scenarios. The
utilization of depth information in RGB-D tracking inspired us to propose a new
method, named MDETrack, which trains a tracking network with an additional
capability to understand the depth of scenes, through supervised or
self-supervised auxiliary Monocular Depth Estimation learning. The outputs of
MDETrack's unified feature extractor are fed to the side-by-side tracking head
and auxiliary depth estimation head, respectively. The auxiliary module will be
discarded in inference, thus keeping the same inference speed. We evaluated our
models with various training strategies on multiple datasets, and the results
show an improved tracking accuracy even without real depth. Through these
findings we highlight the potential of depth estimation in enhancing object
tracking performance.
