用数据驱动的卡尔曼滤波提升车辆多目标跟踪的鲁棒性与实时性
HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking
- 将数据学习的残差与增益融入卡尔曼滤波，自动优化运动模型
- 在KITTI上达82.72% HOTA，处理速度最快达112 FPS
- 无需人工调参，适用于复杂交通场景，适合自动驾驶系统集成
高级驾驶辅助系统（ADAS）的发展推动了对鲁棒且通用的多目标跟踪算法的需求。传统基于统计模型的方法依赖预设运动模型和系统噪声分布假设，虽计算高效，但难以适应多变交通场景，需大量人工设计与参数调优。为此，我们提出一种面向车辆的3D多目标跟踪方法HybridTrack，将数据驱动的卡尔曼滤波（KF）融入检测追踪范式，直接从数据中学习状态转移残差与卡尔曼增益，无需手动建模运动规律与随机参数。在真实世界KITTI数据集上的验证表明，HybridTrack实现82.72%的HOTA准确率，显著优于现有先进方法；在不同配置下评估，最高处理速度达112 FPS。该方法摆脱了对场景特异性设计的依赖，同时提升性能并保持实时性。代码已公开于：https://github.com/leandro-svg/HybridTrack。
原文摘要 · Abstract (English)
The evolution of Advanced Driver Assistance Systems (ADAS) has increased the need for robust and generalizable algorithms for multi-object tracking. Traditional statistical model-based tracking methods rely on predefined motion models and assumptions about system noise distributions. Although computationally efficient, they often lack adaptability to varying traffic scenarios and require extensive manual design and parameter tuning. To address these issues, we propose a novel 3D multi-object tracking approach for vehicles, HybridTrack, which integrates a data-driven Kalman Filter (KF) within a tracking-by-detection paradigm. In particular, it learns the transition residual and Kalman gain directly from data, which eliminates the need for manual motion and stochastic parameter modeling. Validated on the real-world KITTI dataset, HybridTrack achieves 82.72% HOTA accuracy, significantly outperforming state-of-the-art methods. We also evaluate our method under different configurations, achieving the fastest processing speed of 112 FPS. Consequently, HybridTrack eliminates the dependency on scene-specific designs while improving performance and maintaining real-time efficiency. The code is publicly available at: https://github.com/leandro-svg/HybridTrack.
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