Methodologies for incorporating the uncertainties characteristic of data-driven object detectors into object tracking algorithms are explored. Object tracking methods rely on measurement error models, typically in the form of measurement noise, false positive rates, and missed detection rates. Each of these quantities, in general, can be dependent on object or measurement location. However, for detections generated from neural-network processed camera inputs, these measurement error statistics are not sufficient to represent the primary source of errors, namely a dissimilarity between run-time sensor input and the training data upon which the detector was trained. To this end, we investigate incorporating data uncertainty into object tracking methods such as to improve the ability to track objects, and particularly those which out-of-distribution w.r.t. training data. The proposed methodologies are validated on an object tracking benchmark as well on experiments with a real autonomous aircraft.

中文翻译：

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在对象跟踪算法中加入数据不确定性

探索了将数据驱动对象检测器的不确定性特征纳入对象跟踪算法的方法。对象跟踪方法依赖于测量误差模型，通常以测量噪声、误报率和漏检率的形式存在。通常，这些量中的每一个都可以取决于对象或测量位置。然而，对于从神经网络处理的相机输入生成的检测，这些测量误差统计数据不足以代表误差的主要来源，即运行时传感器输入与训练检测器的训练数据之间的不同。为此，我们研究将数据不确定性纳入对象跟踪方法，例如提高跟踪对象的能力，尤其是那些分布外的训练数据。所提出的方法在对象跟踪基准以及真实自主飞机的实验中得到验证。