  An unsupervised online object tracking method that exploits both foreground
and background correlations is proposed and named UHP-SOT (Unsupervised
High-Performance Single Object Tracker) in this work. UHP-SOT consists of three
modules: 1) appearance model update, 2) background motion modeling, and 3)
trajectory-based box prediction. A state-of-the-art discriminative correlation
filters (DCF) based tracker is adopted by UHP-SOT as the first module. We point
out shortcomings of using the first module alone such as failure in recovering
from tracking loss and inflexibility in object box adaptation and then propose
the second and third modules to overcome them. Both are novel in single object
tracking (SOT). We test UHP-SOT on two popular object tracking benchmarks,
TB-50 and TB-100, and show that it outperforms all previous unsupervised SOT
methods, achieves a performance comparable with the best supervised
deep-learning-based SOT methods, and operates at a fast speed (i.e. 22.7-32.0
FPS on a CPU).
