Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking
Single-stream architectures using Vision Transformer (ViT) backbones show
great potential for real-time UAV tracking recently. However, frequent
occlusions from obstacles like buildings and trees expose a major drawback:
these models often lack strategies to handle occlusions effectively. New
methods are needed to enhance the occlusion resilience of single-stream ViT
models in aerial tracking. In this work, we propose to learn Occlusion-Robust
Representations (ORR) based on ViTs for UAV tracking by enforcing an invariance
of the feature representation of a target with respect to random masking
operations modeled by a spatial Cox process. Hopefully, this random masking
approximately simulates target occlusions, thereby enabling us to learn ViTs
that are robust to target occlusion for UAV tracking. This framework is termed
ORTrack. Additionally, to facilitate real-time applications, we propose an
Adaptive Feature-Based Knowledge Distillation (AFKD) method to create a more
compact tracker, which adaptively mimics the behavior of the teacher model
ORTrack according to the task's difficulty. This student model, dubbed
ORTrack-D, retains much of ORTrack's performance while offering higher
efficiency. Extensive experiments on multiple benchmarks validate the
effectiveness of our method, demonstrating its state-of-the-art performance.
Codes is available at https://github.com/wuyou3474/ORTrack.
680204d6989701bd175cd761
You Wu
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2025-04-18T09:38:14.581Z
66babcdde5904d821f3f9e6b
/avatars/ba2247ad3248c99ce52b2a18fe47b626.svg
you wu
user
WY123L
680204d6989701bd175cd762
Xucheng Wang
680204d6989701bd175cd763
Xiangyang Yang
680204d6989701bd175cd764
Mengyuan Liu
680204d6989701bd175cd765
Dan Zeng
680204d6989701bd175cd766
Hengzhou Ye
680204d6989701bd175cd767
Shuiwang Li
Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking
Single-stream architectures using Vision Transformer (ViT) backbones show
great potential for real-time UAV tracking recently. However, frequent
occlusions from obstacles like buildings and trees expose a major drawback:
these models often lack strategies to handle occlusions effectively. New
methods are needed to enhance the occlusion resilience of single-stream ViT
models in aerial tracking. In this work, we propose to learn Occlusion-Robust
Representations (ORR) based on ViTs for UAV tracking by enforcing an invariance
of the feature representation of a target with respect to random masking
operations modeled by a spatial Cox process. Hopefully, this random masking
approximately simulates target occlusions, thereby enabling us to learn ViTs
that are robust to target occlusion for UAV tracking. This framework is termed
ORTrack. Additionally, to facilitate real-time applications, we propose an
Adaptive Feature-Based Knowledge Distillation (AFKD) method to create a more
compact tracker, which adaptively mimics the behavior of the teacher model
ORTrack according to the task's difficulty. This student model, dubbed
ORTrack-D, retains much of ORTrack's performance while offering higher
efficiency. Extensive experiments on multiple benchmarks validate the
effectiveness of our method, demonstrating its state-of-the-art performance.
Codes is available at https://github.com/wuyou3474/ORTrack.
A new framework, ORTrack, using Vision Transformers and random masking to enhance occlusion resilience in real-time UAV tracking, achieves state-of-the-art performance.
Vision Transformer ViT occlusion resilience random masking spatial Cox process occlusion-robust representations ORR feature-based knowledge distillation AFKD ORTrack ORTrack-D