  A key capability of a long-term tracker is to search for targets in very
large areas (typically the entire image) to handle possible target absences or
tracking failures. However, currently there is a lack of such a strong baseline
for global instance search. In this work, we aim to bridge this gap.
Specifically, we propose GlobalTrack, a pure global instance search based
tracker that makes no assumption on the temporal consistency of the target's
positions and scales. GlobalTrack is developed based on two-stage object
detectors, and it is able to perform full-image and multi-scale search of
arbitrary instances with only a single query as the guide. We further propose a
cross-query loss to improve the robustness of our approach against distractors.
With no online learning, no punishment on position or scale changes, no scale
smoothing and no trajectory refinement, our pure global instance search based
tracker achieves comparable, sometimes much better performance on four
large-scale tracking benchmarks (i.e., 52.1% AUC on LaSOT, 63.8% success rate
on TLP, 60.3% MaxGM on OxUvA and 75.4% normalized precision on TrackingNet),
compared to state-of-the-art approaches that typically require complex
post-processing. More importantly, our tracker runs without cumulative errors,
i.e., any type of temporary tracking failures will not affect its performance
on future frames, making it ideal for long-term tracking. We hope this work
will be a strong baseline for long-term tracking and will stimulate future
works in this area. Code is available at
https://github.com/huanglianghua/GlobalTrack.
