标题： YOLOv8-SMOT：一种通过切片辅助训练和自适应关联实现实时小目标跟踪的高效且鲁棒的框架 显示英文标题

标题： YOLOv8-SMOT: An Efficient and Robust Framework for Real-Time Small Object Tracking via Slice-Assisted Training and Adaptive Association

摘要： 从无人机（UAV）视角跟踪小型、灵活的多目标（SMOT），例如鸟类，是一项极具挑战性的计算机视觉任务。困难主要来自三个方面：目标外观特征的极端稀缺性，由相机和目标自身组合动力学引起的复杂运动纠缠，以及由于密集群集行为引起的频繁遮挡和身份模糊。本文详细介绍了我们在MVA 2025“寻找鸟类”小型多目标跟踪挑战赛（SMOT4SB）中夺冠的解决方案，该方案采用了检测跟踪范式，并在检测和关联层面进行了有针对性的创新。在检测方面，我们提出了一种名为\textbf{切片训练}的系统训练增强框架。该框架通过“确定性全覆盖切片”和“切片级随机增强”的协同作用，有效解决了高分辨率图像训练中对小目标学习不足的问题。在跟踪方面，我们设计了一个完全独立于外观信息的鲁棒跟踪器。通过将\textbf{运动方向保持（EMA）}机制和结合\textbf{边界框扩展和距离惩罚}的\textbf{自适应相似性度量}集成到OC-SORT框架中，我们的跟踪器可以稳定处理不规则运动并保持目标身份。我们的方法在SMOT4SB公开测试集上达到了\textbf{55.205}的SO-HOTA分数，充分验证了我们的框架在解决复杂现实世界SMOT问题中的有效性和先进性。源代码将在https://github.com/Salvatore-Love/YOLOv8-SMOT 上提供。 显示英文摘要

摘要： Tracking small, agile multi-objects (SMOT), such as birds, from an Unmanned Aerial Vehicle (UAV) perspective is a highly challenging computer vision task. The difficulty stems from three main sources: the extreme scarcity of target appearance features, the complex motion entanglement caused by the combined dynamics of the camera and the targets themselves, and the frequent occlusions and identity ambiguity arising from dense flocking behavior. This paper details our championship-winning solution in the MVA 2025 "Finding Birds" Small Multi-Object Tracking Challenge (SMOT4SB), which adopts the tracking-by-detection paradigm with targeted innovations at both the detection and association levels. On the detection side, we propose a systematic training enhancement framework named \textbf{SliceTrain}. This framework, through the synergy of 'deterministic full-coverage slicing' and 'slice-level stochastic augmentation, effectively addresses the problem of insufficient learning for small objects in high-resolution image training. On the tracking side, we designed a robust tracker that is completely independent of appearance information. By integrating a \textbf{motion direction maintenance (EMA)} mechanism and an \textbf{adaptive similarity metric} combining \textbf{bounding box expansion and distance penalty} into the OC-SORT framework, our tracker can stably handle irregular motion and maintain target identities. Our method achieves state-of-the-art performance on the SMOT4SB public test set, reaching an SO-HOTA score of \textbf{55.205}, which fully validates the effectiveness and advancement of our framework in solving complex real-world SMOT problems. The source code will be made available at https://github.com/Salvatore-Love/YOLOv8-SMOT.