论文标题
朝PAC多对象检测和跟踪
Towards PAC Multi-Object Detection and Tracking
论文作者
论文摘要
准确地检测和跟踪多对象对于自动导航等安全至关重要的应用很重要。但是，为基于深度学习的最先进技术的性能提供保证仍然具有挑战性。我们考虑一种称为共形预测的策略，该策略预测标签集而不是单个标签。在分类和回归设置中，这些算法可以保证，真正的标签在于以高概率的预测集中。在这些想法的基础上，我们提出了可能具有近似正确（PAC）保证的多对象检测和跟踪算法。他们通过在每个对象检测以及边缘过渡的集合周围构造一个预测来做到这一点；给定对象，检测预测集包含其具有高概率的真实边界框，而边缘预测集包含其在跨帧的真实过渡，具有高概率。我们从经验上证明，我们的方法可以在可可和MOT-17数据集中使用PAC来检测和跟踪对象。
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.
