MMOT：首个面向无人机多光谱多目标跟踪的挑战性基准
MMOT: The First Challenging Benchmark for Drone-based Multispectral Multi-Object Tracking
AI总结：
针对无人机多目标跟踪中RGB算法在航拍下性能退化的问题，提出首个多光谱多目标跟踪基准MMOT，并设计光谱与方向感知的跟踪方案，显著提升小目标和密集场景的跟踪性能。
摘要：
基于无人机的多目标跟踪至关重要，但由于目标小、遮挡严重和背景杂乱，其实现极具挑战性。现有的基于RGB的跟踪算法严重依赖颜色和纹理等空间外观线索，这些线索在航拍视角下常常退化，从而影响可靠性。多光谱成像能够捕获像素级的光谱反射率，在空间条件退化的情况下提供增强目标可区分性的关键线索。然而，缺乏专门的多光谱无人机数据集阻碍了这一领域的发展。为弥补这一空白，我们提出了MMOT，这是首个面向无人机多光谱多目标跟踪的挑战性基准。它具有三个关键特性：（i）大规模——包含125个视频序列，涵盖八个类别，超过48.88万个标注；（ii）全面挑战——覆盖多种条件，如极端小目标、高密度场景、严重遮挡和复杂运动；（iii）精确的定向标注——在航拍视角下实现准确定位并减少歧义。为了更好地提取光谱特征并利用定向标注，我们进一步提出了一种适应现有方法的多光谱和方向感知的多目标跟踪方案，其特点包括：（i）轻量级光谱3D主干网络，在保持与RGB预训练兼容性的同时整合光谱特征；（ii）方向感知的卡尔曼滤波器，用于精确的状态估计；（iii）端到端的方向自适应Transformer。在代表性跟踪器上的大量实验一致表明，与RGB基线相比，多光谱输入显著提升了跟踪性能，尤其对于小而密集的目标。我们相信，我们的工作将推动基于无人机的多光谱多目标跟踪研究。我们的MMOT、代码和基准可在https://github.com/Annzstbl/MMOT公开获取。
英文摘要：
Drone-based multi-object tracking is essential yet highly challenging due to small targets, severe occlusions, and cluttered backgrounds. Existing RGB-based tracking algorithms heavily depend on spatial appearance cues such as color and texture, which often degrade in aerial views, compromising reliability. Multispectral imagery, capturing pixel-level spectral reflectance, provides crucial cues that enhance object discriminability under degraded spatial conditions. However, the lack of dedicated multispectral UAV datasets has hindered progress in this domain. To bridge this gap, we introduce MMOT, the first challenging benchmark for drone-based multispectral multi-object tracking. It features three key characteristics: (i) Large Scale - 125 video sequences with over 488.8K annotations across eight categories; (ii) Comprehensive Challenges - covering diverse conditions such as extreme small targets, high-density scenarios, severe occlusions, and complex motion; and (iii) Precise Oriented Annotations - enabling accurate localization and reduced ambiguity under aerial perspectives. To better extract spectral features and leverage oriented annotations, we further present a multispectral and orientation-aware MOT scheme adapting existing methods, featuring: (i) a lightweight Spectral 3D-Stem integrating spectral features while preserving compatibility with RGB pretraining; (ii) an orientation-aware Kalman filter for precise state estimation; and (iii) an end-to-end orientation-adaptive transformer. Extensive experiments across representative trackers consistently show that multispectral input markedly improves tracking performance over RGB baselines, particularly for small and densely packed objects. We believe our work will advance drone-based multispectral multi-object tracking research. Our MMOT, code, and benchmarks are publicly available at https://github.com/Annzstbl/MMOT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
