Accurately detecting and tracking multi-objects is important for safety-critical applications such as autonomous navigation. However, it remains challenging to provide guarantees on the performance of state-of-the-art techniques based on deep learning. We consider a strategy known as conformal prediction, which predicts sets of labels instead of a single label; in the classification and regression settings, these algorithms can guarantee that the true label lies within the prediction set with high probability. Building on these ideas, we propose multi-object detection and tracking algorithms that come with probably approximately correct (PAC) guarantees. They do so by constructing both a prediction set around each object detection as well as around the set of edge transitions; given an object, the detection prediction set contains its true bounding box with high probability, and the edge prediction set contains its true transition across frames with high probability. We empirically demonstrate that our method can detect and track objects with PAC guarantees on the COCO and MOT-17 datasets.

翻译：对自主导航等安全关键应用来说,准确探测和跟踪多对象非常重要。然而,对基于深层学习的先进技术的性能提供保障仍然具有挑战性。我们考虑一种被称为符合预测的战略,即预测标签,而不是单一标签;在分类和回归设置中,这些算法可以保证真实标签在预测中具有很高概率。基于这些想法,我们提出多对象探测和跟踪算法,这些算法可能具有大致正确(PAC)的保证。它们通过围绕每个物体探测和边缘过渡组建立预测组来这样做;考虑到一个物体,探测预测组包含真实的捆绑框,概率很高,边缘预测组包含其真实的跨框架过渡,概率很高。我们从经验上证明,我们的方法可以探测和跟踪具有CO和MOT-17数据集的PAC保证对象。