用于稳健无人机跟踪的反事实运动可靠性学习

中文摘要 AI 辅助

红外无人机跟踪具有挑战性，目标小、对比度低且易与热干扰物或杂乱背景混淆。基于Transformer的跟踪器虽有进展，但目标外观弱或模糊时易受背景主导。引入运动线索是自然解决办法，但红外无人机跟踪中运动线索不可靠。为此提出CMRTrack框架，用轻量级运动证据编码器提取时间证据，训练时引入反事实目标擦除历史分支构建硬运动参考，将学习到的运动证据通过运动引导令牌调制和可靠性感知分数融合纳入单流跟踪框架。在Anti-UAV410上的大量实验表明CMRTrack优于现有跟踪器，消融研究和定性分析验证了反事实运动可靠性学习的有效性。

英文摘要

Infrared unmanned aerial vehicle (UAV) tracking is challenging because the target is often small, low-contrast, and easily confused with thermal distractors or cluttered backgrounds. Recent Transformer-based trackers have achieved promising performance by learning strong appearance representations, but their responses can still be dominated by background structures when the target appearance is weak or ambiguous. A natural solution is to introduce temporal motion cues. However, in infrared UAV tracking, motion cues are not always reliable: camera jitter, dynamic backgrounds, sensor noise, and target disappearance may produce temporal variations that are stronger than the true target motion. Therefore, the key challenge is not simply how to use motion, but how to distinguish target-consistent motion from background-induced pseudo motion. To this end, we propose CMRTrack, a counterfactual motion reliability learning framework for robust infrared UAV tracking. CMRTrack first extracts temporal evidence from adjacent search regions using a lightweight motion evidence encoder. During training, a counterfactual target-erased history branch is introduced to construct hard motion references, encouraging the motion encoder to learn reliable target-consistent motion rather than arbitrary temporal changes. The learned motion evidence is then incorporated into a one-stream tracking framework through motion-guided token modulation and reliability-aware score fusion, enabling adaptive feature enhancement and response refinement. Extensive experiments on Anti-UAV410 demonstrate that CMRTrack consistently outperforms representative state-of-the-art trackers and significantly improves the OSTrack baseline, with ablation studies and qualitative analysis verifying the effectiveness of the proposed counterfactual motion reliability learning.

发表机构

• Institute for Low-Altitude Regulation, Xi’an Jiaotong University（西安交通大学低空调控研究所）
• Lanzhou University（兰州大学）

机构由 AI 辅助整理，请以论文原文为准。