  Supervised and unsupervised deep trackers that rely on deep learning
technologies are popular in recent years. Yet, they demand high computational
complexity and a high memory cost. A green unsupervised single-object tracker,
called GUSOT, that aims at object tracking for long videos under a
resource-constrained environment is proposed in this work. Built upon a
baseline tracker, UHP-SOT++, which works well for short-term tracking, GUSOT
contains two additional new modules: 1) lost object recovery, and 2)
color-saliency-based shape proposal. They help resolve the tracking loss
problem and offer a more flexible object proposal, respectively. Thus, they
enable GUSOT to achieve higher tracking accuracy in the long run. We conduct
experiments on the large-scale dataset LaSOT with long video sequences, and
show that GUSOT offers a lightweight high-performance tracking solution that
finds applications in mobile and edge computing platforms.
