OneTracker: Unifying Visual Object Tracking with Foundation Models and
  Efficient Tuning
Lingyi Hong Shilin Yan Renrui Zhang Wanyun Li Xinyu Zhou Pinxue Guo Kaixun Jiang Yiting Chen Jinglun Li Zhaoyu Chen Wenqiang Zhang
Visual object tracking aims to localize the target object of each frame based
on its initial appearance in the first frame. Depending on the input modility,
tracking tasks can be divided into RGB tracking and RGB+X (e.g. RGB+N, and
RGB+D) tracking. Despite the different input modalities, the core aspect of
tracking is the temporal matching. Based on this common ground, we present a
general framework to unify various tracking tasks, termed as OneTracker.
OneTracker first performs a large-scale pre-training on a RGB tracker called
Foundation Tracker. This pretraining phase equips the Foundation Tracker with a
stable ability to estimate the location of the target object. Then we regard
other modality information as prompt and build Prompt Tracker upon Foundation
Tracker. Through freezing the Foundation Tracker and only adjusting some
additional trainable parameters, Prompt Tracker inhibits the strong
localization ability from Foundation Tracker and achieves parameter-efficient
finetuning on downstream RGB+X tracking tasks. To evaluate the effectiveness of
our general framework OneTracker, which is consisted of Foundation Tracker and
Prompt Tracker, we conduct extensive experiments on 6 popular tracking tasks
across 11 benchmarks and our OneTracker outperforms other models and achieves
state-of-the-art performance.