Existing Visual Object Tracking (VOT) only takes the target area in the first
frame as a template. This causes tracking to inevitably fail in fast-changing
and crowded scenes, as it cannot account for changes in object appearance
between frames. To this end, we revamped the tracking framework with
Progressive Context Encoding Transformer Tracker (ProContEXT), which coherently
exploits spatial and temporal contexts to predict object motion trajectories.
Specifically, ProContEXT leverages a context-aware self-attention module to
encode the spatial and temporal context, refining and updating the multi-scale
static and dynamic templates to progressively perform accurate tracking. It
explores the complementary between spatial and temporal context, raising a new
pathway to multi-context modeling for transformer-based trackers. In addition,
ProContEXT revised the token pruning technique to reduce computational
complexity. Extensive experiments on popular benchmark datasets such as GOT-10k
and TrackingNet demonstrate that the proposed ProContEXT achieves
state-of-the-art performance.

本研究提出了一种基于 Progressive Context Encoding 的可变模板追踪，利用空间和时间上下文预测物体运动轨迹，同时探索了多种上下文的建模方法并在多个基准数据集上获得了最先进的性能。