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.

多目标追踪是自主驾驶中的关键技术之一。本文提出了一种用于处理复杂交通场景中的异常值、提高追踪准确性的算法，称之为卷积 UKF。该算法在 KITTI 和 nuScenes 数据集上的实验结果表明，相对于基准算法，卷积 UKF 能够实现更高的追踪准确性。

多目标跟踪中的卷积无味扩展卡尔曼滤波器

Convolutional Unscented Kalman Filter for Multi-Object Tracking with  Outliers

Traditional tracking-by-detection systems typically employ Kalman filters
(KF) for state estimation. However, the KF requires domain-specific design
choices and it is ill-suited to handling non-linear motion patterns. To address
these limitations, we propose two innovative data-driven filtering methods. Our
first method employs a Bayesian filter with a trainable motion model to predict
an object's future location and combines its predictions with observations
gained from an object detector to enhance bounding box prediction accuracy.
Moreover, it dispenses with most domain-specific design choices characteristic
of the KF. The second method, an end-to-end trainable filter, goes a step
further by learning to correct detector errors, further minimizing the need for
domain expertise. Additionally, we introduce a range of motion model
architectures based on Recurrent Neural Networks, Neural Ordinary Differential
Equations, and Conditional Neural Processes, that are combined with the
proposed filtering methods. Our extensive evaluation across multiple datasets
demonstrates that our proposed filters outperform the traditional KF in object
tracking, especially in the case of non-linear motion patterns -- the use case
our filters are best suited to. We also conduct noise robustness analysis of
our filters with convincing positive results. We further propose a new cost
function for associating observations with tracks. Our tracker, which
incorporates this new association cost with our proposed filters, outperforms
the conventional SORT method and other motion-based trackers in multi-object
tracking according to multiple metrics on motion-rich DanceTrack and SportsMOT
datasets.

我们提出了两种创新的数据驱动滤波方法，通过结合可训练的运动模型进行对象位置预测，并将观察结果与对象检测器的预测相结合，以提高边界框预测准确性。我们的方法在多个数据集上进行了广泛评估，证明在对象跟踪方面，尤其是非线性运动模式的情况下，我们的滤波器优于传统的 Kalman 滤波器。