DMTrack：面向无人机多目标跟踪的可变形状态空间建模，融合卡尔曼滤波与不确定性感知关联
DMTrack: Deformable State-Space Modeling for UAV Multi-Object Tracking with Kalman Fusion and Uncertainty-Aware Association
AI总结：
针对无人机多目标跟踪中运动复杂、身份易切换的问题，提出融合可变形状态空间预测、卡尔曼门控融合与不确定性关联的DMTrack框架，在基准上实现了最优跟踪性能与高效推理。
摘要：
无人机（UAV）视角下的多目标跟踪（MOT）面临独特挑战：航拍视角固有的目标运动不可预测、遮挡频繁、外观线索有限。无人机的突发机动会进一步加剧这些问题，导致轨迹估计不可靠、身份切换频发。传统运动模型（如卡尔曼滤波器或静态序列编码器）往往难以同时捕捉此类场景下的线性和非线性动力学特性。针对这些局限，我们提出DMTrack——一个专为无人机多目标跟踪设计的可变形运动跟踪框架。DMTrack包含三个核心组件：DeformMamba（可变形状态空间预测器），可动态聚合历史运动状态以实现自适应轨迹建模；MotionGate（轻量级门控模块），基于运动上下文和不确定性融合卡尔曼与Mamba的预测结果；以及不确定性感知关联策略，通过对齐运动趋势与预测置信度提升身份保持能力。在VisDrone-MOT和UAVDT基准上的大量实验表明，DMTrack在身份一致性和跟踪精度上达到了当前最优性能，尤其在高速和非线性运动场景下表现突出。值得注意的是，该方法无需外观模型且保持了具有竞争力的效率，凸显了其在鲁棒无人机跟踪中的实用性。
英文摘要：
Multi-object tracking (MOT) from unmanned aerial vehicles (UAVs) presents unique challenges due to unpredictable object motion, frequent occlusions, and limited appearance cues inherent to aerial viewpoints. These issues are further exacerbated by abrupt UAV movements, leading to unreliable trajectory estimation and identity switches. Conventional motion models, such as Kalman filters or static sequence encoders, often fall short in capturing both linear and non-linear dynamics under such conditions. To tackle these limitations, we propose DMTrack, a deformable motion tracking framework tailored for UAV-based MOT. Our DMTrack introduces three key components: DeformMamba, a deformable state-space predictor that dynamically aggregates historical motion states for adaptive trajectory modeling; MotionGate, a lightweight gating module that fuses Kalman and Mamba predictions based on motion context and uncertainty; and an uncertainty-aware association strategy that enhances identity preservation by aligning motion trends with prediction confidence. Extensive experiments on the VisDrone-MOT and UAVDT benchmarks demonstrate that our DMTrack achieves state-of-the-art performance in identity consistency and tracking accuracy, particularly under high-speed and non-linear motion. Importantly, our method operates without appearance models and maintains competitive efficiency, highlighting its practicality for robust UAV-based tracking.
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。
