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An enhanced version of UHP-SOT called UHP-SOT++ is proposed for unsupervised, lightweight and high-performance single object tracking in this work. Both UHP-SOT and UHP-SOT++ exploit the discriminative-correlation-filters-based (DCF-based) tracker as their baseline and incorporate two new ingredients: (1) background motion modeling and (2) object box trajectory modeling. Their difference lies in 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 robust tracking performance against large-scale tracking datasets. Extensive evaluation of state-of-the-art supervised/unsupervised deep and unsupervised lightweight trackers is conducted on four SOT benchmark datasets – OTB2015, TC128, UAV123 and LaSOT. UHP-SOT++ achieves outstanding tracking performance with a small model size and low computational complexity (i.e., operating at a rate of 20 FPS on an i5 CPU even without code optimization). UHP-SOT++ offers an ideal solution in real-time object tracking on resource-limited platforms. Finally, we compare the pros and cons of supervised deep trackers and unsupervised lightweight trackers and provide a new perspective to their performance gap.