UncTrack: Reliable Visual Object Tracking with Uncertainty-Aware Prototype Memory Network
Journal:
arXiv
Published Date:
Mar 17, 2025
Abstract
Transformer-based trackers have achieved promising success and become the
dominant tracking paradigm due to their accuracy and efficiency. Despite the
substantial progress, most of the existing approaches tackle object tracking as
a deterministic coordinate regression problem, while the target localization
uncertainty has been greatly overlooked, which hampers trackers' ability to
maintain reliable target state prediction in challenging scenarios. To address
this issue, we propose UncTrack, a novel uncertainty-aware transformer tracker
that predicts the target localization uncertainty and incorporates this
uncertainty information for accurate target state inference. Specifically,
UncTrack utilizes a transformer encoder to perform feature interaction between
template and search images. The output features are passed into an
uncertainty-aware localization decoder (ULD) to coarsely predict the
corner-based localization and the corresponding localization uncertainty. Then
the localization uncertainty is sent into a prototype memory network (PMN) to
excavate valuable historical information to identify whether the target state
prediction is reliable or not. To enhance the template representation, the
samples with high confidence are fed back into the prototype memory bank for
memory updating, making the tracker more robust to challenging appearance
variations. Extensive experiments demonstrate that our method outperforms other
state-of-the-art methods. Our code is available at
https://github.com/ManOfStory/UncTrack.
