EECTracker：基于集群运动先验引导特征补偿的机载光学无人机集群跟踪
EECTracker: Swarm Motion Prior-Guided Feature Compensation for Airborne Optical UAV Swarm Tracking
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中文总结 AI 辅助
针对机载光学无人机集群跟踪中目标特征响应弱导致轨迹碎片化问题，提出EECTracker框架，利用集群运动先验引导特征补偿，在AIRMOT和UAVSwarm上显著提升跟踪性能。
中文摘要 AI 辅助
机载光学跟踪无人机集群因目标尺度极小、视角快速变化和背景杂乱而具有挑战性，这些因素会削弱目标特征响应，导致检测器响应间歇性或暂时缺失。现有的多目标跟踪方法通常依赖可靠的目标特定检测器响应来维持跨帧的目标状态和身份。当这些响应变得不可靠时，目标状态无法可靠更新，跨帧关联线索变得模糊，导致轨迹碎片化和身份切换。为解决此问题，我们提出EECTracker，一种集群运动先验引导的联合检测与跟踪框架，用于机载光学无人机集群跟踪。EECTracker从可靠的历史轨迹片段构建概率集群运动先验，以捕获集群共享的短期图像平面运动趋势及其不确定性，为跨帧特征补偿提供空间指导。基于此先验，我们引入能量-熵一致性激活（EEC激活），利用特征残差能量和局部残差熵评估运动先验条件下的特征一致性。得到的局部EEC分数通过增强潜在目标区域中与运动先验一致的特征响应并抑制不一致的背景响应，指导像素级特征补偿。在AIRMOT和UAVSwarm上的实验表明，与最先进方法相比，EECTracker实现了优越的整体跟踪性能。与最强竞争方法SCT-MOT相比，EECTracker在AIRMOT上将MOTA/IDF1提高了3.89/1.79个百分点，在UAVSwarm上提高了2.81/1.74个百分点，同时保持了在线推理速度。
英文摘要
Airborne optical tracking of uncrewed aerial vehicle (UAV) swarms is challenging due to extremely small target scales, rapid viewpoint changes, and cluttered backgrounds, which can weaken target feature responses and lead to intermittent or temporarily missing detector responses. Existing multi-object tracking methods generally depend on reliable target-specific detector responses to maintain target states and identities across frames. When such responses become unreliable, target states cannot be reliably updated and cross-frame association cues become ambiguous, resulting in fragmented trajectories and identity switches. To address this problem, we propose EECTracker, a swarm-motion-prior-guided joint detection-and-tracking framework for airborne optical UAV swarm tracking. EECTracker constructs a probabilistic swarm motion prior from reliable historical tracklets to capture the shared short-term image-plane motion tendency of the swarm and its uncertainty, providing spatial guidance for cross-frame feature compensation. Building on this prior, we introduce Energy--Entropy Consistency Activation (EEC Activation) to evaluate motion-prior-conditioned feature consistency using feature residual energy and local residual entropy. The resulting Local EEC score guides pixel-level feature compensation by enhancing motion-prior-consistent feature responses in potential target regions while suppressing inconsistent background responses. Experiments on AIRMOT and UAVSwarm show that EECTracker achieves superior overall tracking performance compared with state-of-the-art methods. Compared with the strongest competing method SCT-MOT, EECTracker improves MOTA/IDF1 by 3.89/1.79 percentage points on AIRMOT and by 2.81/1.74 percentage points on UAVSwarm, while maintaining online inference speed.
发表机构
- China-UAE Belt and Road Joint Laboratory on Intelligent Unmanned Systems, School of Aerospace Engineering, Beijing Institute of Technology（中国-阿联酋“一带一路”智能无人系统联合实验室，北京理工大学宇航学院）
机构由 AI 辅助整理，请以论文原文为准。
