让无人机追踪模型更抗遮挡，提升实时性与鲁棒性
Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking
- 通过随机掩码模拟遮挡，训练视觉变压器模型保持目标特征不变
- 在多个基准上实现顶尖性能，且新模型效率更高
- 适合需要实时、抗遮挡的无人机视觉追踪场景
近期基于视觉变换器（ViT）的单流架构在实时无人机追踪中展现出巨大潜力。然而，建筑物和树木等障碍物导致的频繁遮挡暴露了其主要缺陷：缺乏有效处理遮挡的策略。本文提出基于ViT的遮挡鲁棒表征学习方法（ORR），通过施加目标特征对随机掩码操作的不变性来增强模型鲁棒性，掩码由空间柯克斯过程建模，近似模拟目标遮挡。该框架命名为ORTrack。为进一步支持实时应用，提出自适应特征知识蒸馏（AFKD）方法，根据任务难度自适应模仿教师模型行为，生成更紧凑的学生模型（ORTrack-D）。该模型在保留原模型性能的同时显著提升效率。多基准实验验证了方法的有效性，达到当前最优水平。代码已公开于https://github.com/wuyou3474/ORTrack。
原文摘要 · Abstract (English)
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.
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