Leveraging the Power of Data Augmentation for Transformer-based Tracking
Jie Zhao Johan Edstedt Michael Felsberg Dong Wang Huchuan Lu
Due to long-distance correlation and powerful pretrained models,
transformer-based methods have initiated a breakthrough in visual object
tracking performance. Previous works focus on designing effective architectures
suited for tracking, but ignore that data augmentation is equally crucial for
training a well-performing model. In this paper, we first explore the impact of
general data augmentations on transformer-based trackers via systematic
experiments, and reveal the limited effectiveness of these common strategies.
Motivated by experimental observations, we then propose two data augmentation
methods customized for tracking. First, we optimize existing random cropping
via a dynamic search radius mechanism and simulation for boundary samples.
Second, we propose a token-level feature mixing augmentation strategy, which
enables the model against challenges like background interference. Extensive
experiments on two transformer-based trackers and six benchmarks demonstrate
the effectiveness and data efficiency of our methods, especially under
challenging settings, like one-shot tracking and small image resolutions.