融合物理模型与深度学习，提升快速小物体的3D跟踪精度
Physics-Guided Fusion for Robust 3D Tracking of Fast Moving Small Objects
- 用物理运动方程建模，增强对遮挡和急转弯场景的鲁棒性
- 在自建网球数据集上，平均位移误差比卡尔曼滤波低70%
- 适合机器人感知、高速运动目标追踪等实时应用
尽管计算机视觉在通用目标检测与跟踪方面取得显著进展，但对高速移动的小型物体的处理仍缺乏深入研究。本文针对使用RGB-D相机检测与跟踪快速移动小物体这一挑战，提出一种结合基于深度学习的检测与基于物理的跟踪的新系统。主要贡献包括：(1) 设计了一套完整的3D空间中快速移动小物体的检测与跟踪系统；(2) 提出一种创新的基于物理的跟踪算法，整合运动学方程以应对异常值和漏检；(3) 设计了异常值检测与修正模块，在遮挡和快速方向变化等复杂场景下显著提升跟踪性能。我们在自建的网球数据集上进行了评估，结果表明，本系统相比基于卡尔曼滤波的跟踪器，平均位移误差降低高达70%。该系统在提升自主平台机器人感知能力方面具有重要应用价值，验证了将物理模型与深度学习结合在实时3D检测与跟踪挑战性小物体上的有效性。
原文摘要 · Abstract (English)
While computer vision has advanced considerably for general object detection and tracking, the specific problem of fast-moving tiny objects remains underexplored. This paper addresses the significant challenge of detecting and tracking rapidly moving small objects using an RGB-D camera. Our novel system combines deep learning-based detection with physics-based tracking to overcome the limitations of existing approaches. Our contributions include: (1) a comprehensive system design for object detection and tracking of fast-moving small objects in 3D space, (2) an innovative physics-based tracking algorithm that integrates kinematics motion equations to handle outliers and missed detections, and (3) an outlier detection and correction module that significantly improves tracking performance in challenging scenarios such as occlusions and rapid direction changes. We evaluated our proposed system on a custom racquetball dataset. Our evaluation shows our system surpassing kalman filter based trackers with up to 70\% less Average Displacement Error. Our system has significant applications for improving robot perception on autonomous platforms and demonstrates the effectiveness of combining physics-based models with deep learning approaches for real-time 3D detection and tracking of challenging small objects.
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