  An unsupervised, lightweight and high-performance single object tracker,
called UHP-SOT, was proposed by Zhou et al. recently. As an extension, we
present an enhanced version and name it UHP-SOT++ in this work. Built upon the
foundation of the discriminative-correlation-filters-based (DCF-based) tracker,
two new ingredients are introduced in UHP-SOT and UHP-SOT++: 1) background
motion modeling and 2) object box trajectory modeling. The main difference
between UHP-SOT and UHP-SOT++ is the fusion strategy of proposals from three
models (i.e., DCF, background motion and object box trajectory models). An
improved fusion strategy is adopted by UHP-SOT++ for more robust tracking
performance against large-scale tracking datasets. Our second contribution lies
in an extensive evaluation of the performance of state-of-the-art supervised
and unsupervised methods by testing them on four SOT benchmark datasets -
OTB2015, TC128, UAV123 and LaSOT. Experiments show that UHP-SOT++ outperforms
all previous unsupervised methods and several deep-learning (DL) methods in
tracking accuracy. Since UHP-SOT++ has extremely small model size, high
tracking performance, and low computational complexity (operating at a rate of
20 FPS on an i5 CPU even without code optimization), it is an ideal solution in
real-time object tracking on resource-limited platforms. Based on the
experimental results, we compare pros and cons of supervised and unsupervised
trackers and provide a new perspective to understand the performance gap
between supervised and unsupervised methods, which is the third contribution of
this work.
