Abstract
Infrared object tracking plays a crucial role in Anti-Unmanned Aerial Vehicle (Anti-UAV) applications. Existing trackers often depend on cropped template regions and have limited motion modeling capabilities, which pose challenges when dealing with tiny targets. To address this, we propose a simple yet effective infrared tiny-object tracker that enhances tracking performance by integrating global detection and motion-aware learning with temporal priors. Our method is based on object detection and achieves significant improvements through two key innovations. First, we introduce frame dynamics, leveraging frame difference and optical flow to encode both prior target features and motion characteristics at the input level, enabling the model to better distinguish the target from background clutter. Second, we propose a trajectory constraint filtering strategy in the post-processing stage, utilizing spatio-temporal priors to suppress false positives and enhance tracking robustness. Extensive experiments show that our method consistently outperforms existing approaches across multiple metrics in challenging infrared UAV tracking scenarios. Notably, we achieve state-of-the-art performance in the 4th Anti-UAV Challenge, securing 1st place in Track 1 and 2nd place in Track 2.
Abstract (translated)
红外目标跟踪在反无人机（Anti-UAV）应用中扮演着至关重要的角色。现有的追踪器通常依赖于裁剪的模板区域，并且具有有限的动作建模能力，这在处理微小目标时会带来挑战。为了解决这些问题，我们提出了一种简单而有效的红外微小物体追踪器，通过整合全局检测和带有时间先验的知识驱动学习来增强跟踪性能。 我们的方法基于对象检测，并通过两个关键创新实现了显著的改进。首先，我们引入了帧动态性，利用帧差分和光流技术在输入层面编码先前的目标特征以及动作特性，从而使模型能够更好地将目标与背景噪声区分开来。其次，在后期处理阶段，我们提出了一种轨迹约束过滤策略，利用时空先验来抑制假阳性并增强跟踪鲁棒性。 广泛的实验表明，我们的方法在具有挑战性的红外无人机追踪场景中以多种度量标准上始终优于现有方法。特别值得注意的是，我们在第四届反无人机挑战赛中取得了最新技术水平的表现，在Track 1和Track 2分别获得了第一名和第二名的成绩。
URL
https://arxiv.org/abs/2505.04917
