title: Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking
summary: 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.
paper_authors._id: 680204d6989701bd175cd761
paper_authors.name: You Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-18T09:38:14.581Z
paper_authors._id: 66babcdde5904d821f3f9e6b
paper_authors.avatarUrl: /avatars/ba2247ad3248c99ce52b2a18fe47b626.svg
paper_authors.fullname: you wu
paper_authors.type: user
paper_authors.user: WY123L
paper_authors._id: 680204d6989701bd175cd762
paper_authors.name: Xucheng Wang
paper_authors._id: 680204d6989701bd175cd763
paper_authors.name: Xiangyang Yang
paper_authors._id: 680204d6989701bd175cd764
paper_authors.name: Mengyuan Liu
paper_authors._id: 680204d6989701bd175cd765
paper_authors.name: Dan Zeng
paper_authors._id: 680204d6989701bd175cd766
paper_authors.name: Hengzhou Ye
paper_authors._id: 680204d6989701bd175cd767
paper_authors.name: Shuiwang Li
paper_title: Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Vision Transformer ViT occlusion resilience random masking spatial Cox process occlusion-robust representations ORR feature-based knowledge distillation AFKD ORTrack ORTrack-D