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。