Long-term visual tracking is a challenging problem.
State-of-the-art long-term trackers, e.g., GlobalTrack,
utilize region proposal networks (RPNs) to generate target
proposals. However, the performance of the trackers is
affected by occlusions and large scale or ratio variations.
To address these issues, in this paper, we are the first to
propose a novel architecture with transformers for
long-term visual tracking. Specifically, the proposed
Visual Tracking Transformer (VTT) utilizes a transformer
encoder-decoder architecture for aggregating global
information to deal with occlusion and large scale or ratio
variation. Furthermore, it also shows better discriminative
power against instance-level distractors without the need
for extra labeling and hard-sample mining. We conduct
extensive experiments on three largest long-term tracking
dataset and have achieved state-of-the-art performance.

VTT: Long-term Visual Tracking with Transformers

**Session Chairs**: prof. Federico Pernici, prof. Fabio Poiesi

OS T3.2: Computer Vision, Robotics and Tracking

technical paper

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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Tianling Bian

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VTT: Long-term Visual Tracking with Transformers

VTT: Long-term Visual Tracking with Transformers

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