Multi-object tracking (MOT) is an essential technique for navigation in autonomous driving. In tracking-by-detection systems, biases, false positives, and misses, which are referred to as outliers, are inevitable due to complex traffic scenarios. Recent tracking methods are based on filtering algorithms that overlook these outliers, leading to reduced tracking accuracy or even loss of the objects trajectory. To handle this challenge, we adopt a probabilistic perspective, regarding the generation of outliers as misspecification between the actual distribution of measurement data and the nominal measurement model used for filtering. We further demonstrate that, by designing a convolutional operation, we can mitigate this misspecification. Incorporating this operation into the widely used unscented Kalman filter (UKF) in commonly adopted tracking algorithms, we derive a variant of the UKF that is robust to outliers, called the convolutional UKF (ConvUKF). We show that ConvUKF maintains the Gaussian conjugate property, thus allowing for real-time tracking. We also prove that ConvUKF has a bounded tracking error in the presence of outliers, which implies robust stability. The experimental results on the KITTI and nuScenes datasets show improved accuracy compared to representative baseline algorithms for MOT tasks.

翻译：多目标跟踪是自动驾驶导航中的关键技术。在基于检测的跟踪系统中，由于复杂的交通场景，偏差、误检和漏检（统称为异常值）不可避免。现有的跟踪方法多基于滤波算法，这些算法往往忽略异常值，导致跟踪精度下降甚至目标轨迹丢失。为解决这一问题，我们从概率视角出发，将异常值的产生视为测量数据实际分布与滤波所用名义测量模型之间的失配。我们进一步证明，通过设计卷积操作可以缓解这种失配。将这一操作整合到常用跟踪算法中广泛采用的无迹卡尔曼滤波中，我们推导出一种对异常值具有鲁棒性的UKF变体，称为卷积UKF。我们证明ConvUKF保持了高斯共轭特性，因此能够实现实时跟踪。同时，我们证明了在存在异常值的情况下ConvUKF具有有界跟踪误差，这意味着其具备鲁棒稳定性。在KITTI和nuScenes数据集上的实验结果表明，与多目标跟踪任务中的代表性基线算法相比，该方法获得了更高的精度。