Robust Object Modeling for Visual Tracking
Yidong Cai Jie Liu Jie Tang Gangshan Wu
Object modeling has become a core part of recent tracking frameworks. Current
popular tackers use Transformer attention to extract the template feature
separately or interactively with the search region. However, separate template
learning lacks communication between the template and search regions, which
brings difficulty in extracting discriminative target-oriented features. On the
other hand, interactive template learning produces hybrid template features,
which may introduce potential distractors to the template via the cluttered
search regions. To enjoy the merits of both methods, we propose a robust object
modeling framework for visual tracking (ROMTrack), which simultaneously models
the inherent template and the hybrid template features. As a result, harmful
distractors can be suppressed by combining the inherent features of target
objects with search regions' guidance. Target-related features can also be
extracted using the hybrid template, thus resulting in a more robust object
modeling framework. To further enhance robustness, we present novel variation
tokens to depict the ever-changing appearance of target objects. Variation
tokens are adaptable to object deformation and appearance variations, which can
boost overall performance with negligible computation. Experiments show that
our ROMTrack sets a new state-of-the-art on multiple benchmarks.