Visual tracking has made significant improvements in the past few decades.
Most existing state-of-the-art trackers 1) merely aim for performance in ideal
conditions while overlooking the real-world conditions; 2) adopt the
tracking-by-detection paradigm, neglecting rich temporal contexts; 3) only
integrate the temporal information into the template, where temporal contexts
among consecutive frames are far from being fully utilized. To handle those
problems, we propose a two-level framework (TCTrack) that can exploit temporal
contexts efficiently. Based on it, we propose a stronger version for real-world
visual tracking, i.e., TCTrack++. It boils down to two levels: features and
similarity maps. Specifically, for feature extraction, we propose an
attention-based temporally adaptive convolution to enhance the spatial features
using temporal information, which is achieved by dynamically calibrating the
convolution weights. For similarity map refinement, we introduce an adaptive
temporal transformer to encode the temporal knowledge efficiently and decode it
for the accurate refinement of the similarity map. To further improve the
performance, we additionally introduce a curriculum learning strategy. Also, we
adopt online evaluation to measure performance in real-world conditions.
Exhaustive experiments on 8 wellknown benchmarks demonstrate the superiority of
TCTrack++. Real-world tests directly verify that TCTrack++ can be readily used
in real-world applications.

提出了一种利用时间上下文的双层框架 (TCTrack)，用于实现更强大的现实世界视觉跟踪，并在特征提取和相似度图优化方面进行了改进。