用于基于事件的无人机预测的残差卡尔曼动力学
Residual Kalman Dynamics for Event-Based UAV Forecasting
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中文总结 AI 辅助
该研究在FRED数据集上针对无人机边界框预测，提出结合卡尔曼滤波与残差模型的方法，发现残差目标可部分由锚点位置速度预测，且事件条件残差模型在去相关子集测试中仍具预测能力。
中文摘要 AI 辅助
我们在FRED事件相机数据集上研究短程和中程无人机边界框预测。我们采用全中心尺寸框状态下的恒速卡尔曼滤波器作为强物理基线，并训练残差模型，从近期框历史、滤波后的状态特征以及局部事件表示中预测类加速度修正。这种简单的残差公式始终优于卡尔曼基线，其中事件条件模型在评估方法中取得最强结果。我们进一步表明，部分残差目标仅可从锚点位置和速度预测，这表明标准FRED结果可同时反映视觉证据和数据集特定的运动先验。为分析此效应，我们引入去相关子集作为诊断压力测试，结果显示，即使基于测量位置和速度的捷径被削弱，事件条件残差模型仍保留有用的预测信号。
英文摘要
We study short- and mid-horizon UAV bounding-box forecasting on the FRED event-camera dataset. We use a constant-velocity Kalman filter over a full center-size box state as a strong physical baseline, and train a residual model to predict acceleration-like corrections from recent box history, filtered state features, and local event representations. This simple residual formulation consistently improves over the Kalman baseline, with event-conditioned models giving the strongest results among the evaluated methods. We further show that part of the residual target is predictable from anchor position and velocity alone, indicating that canonical FRED results can reflect both visual evidence and dataset-specific motion priors. To analyze this effect, we introduce decorrelated subsets as a diagnostic stress test, showing that event-conditioned residual models retain useful predictive signal even when measured position- and velocity-based shortcuts are weakened.
发表机构
- Swedish Defence Research Agency (FOI)（瑞典国防研究局（FOI））
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