ODTrack: Online Dense Temporal Token Learning for Visual Tracking
Yaozong Zheng Bineng Zhong Qihua Liang Zhiyi Mo Shengping Zhang Xianxian Li
Online contextual reasoning and association across consecutive video frames
are critical to perceive instances in visual tracking. However, most current
top-performing trackers persistently lean on sparse temporal relationships
between reference and search frames via an offline mode. Consequently, they can
only interact independently within each image-pair and establish limited
temporal correlations. To alleviate the above problem, we propose a simple,
flexible and effective video-level tracking pipeline, named \textbf{ODTrack},
which densely associates the contextual relationships of video frames in an
online token propagation manner. ODTrack receives video frames of arbitrary
length to capture the spatio-temporal trajectory relationships of an instance,
and compresses the discrimination features (localization information) of a
target into a token sequence to achieve frame-to-frame association. This new
solution brings the following benefits: 1) the purified token sequences can
serve as prompts for the inference in the next video frame, whereby past
information is leveraged to guide future inference; 2) the complex online
update strategies are effectively avoided by the iterative propagation of token
sequences, and thus we can achieve more efficient model representation and
computation. ODTrack achieves a new \textit{SOTA} performance on seven
benchmarks, while running at real-time speed. Code and models are available at
\url{https://github.com/GXNU-ZhongLab/ODTrack}.