Classification of movement trajectories has many applications in transportation. Supervised neural models represent the current state-of-the-art. Recent security applications require this task to be rapidly employed in environments that may differ from the data used to train such models for which there is little training data. We provide a neuro-symbolic rule-based framework to conduct error correction and detection of these models to support eventual deployment in security applications. We provide a suite of experiments on several recent and state-of-the-art models and show an accuracy improvement of 1.7% over the SOTA model in the case where all classes are present in training and when 40% of classes are omitted from training, we obtain a 5.2% improvement (zero-shot) and 23.9% (few-shot) improvement over the SOTA model without resorting to retraining of the base model.

翻译：运动轨迹分类在交通领域具有广泛应用。当前最先进的方法主要采用监督式神经模型。近期安全应用场景要求该任务能快速部署在与训练数据可能存在差异的环境中，且此类环境通常缺乏充足训练数据。我们提出了一种神经符号规则框架，用于对这些模型进行错误检测与修正，以支持其在安全场景中的最终部署。通过针对多个最新先进模型开展系列实验，我们验证了该方法的有效性：当训练数据包含全部类别时，模型准确率较当前最优模型提升1.7%；当训练数据缺失40%类别时，在不重新训练基础模型的前提下，零样本学习场景下准确率提升5.2%，小样本学习场景下提升23.9%。