Counterfactual Motion Reliability Learning for Robust UAV Tracking
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 motio...
| Authors | Yuehai Chen |
|---|---|
| Primary category | cs.CV |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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