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。有监督神经模型代表了当前最先进的技术水平。近期安全应用要求该任务能够在与模型训练数据可能存在差异的环境中快速部署，而此类环境中的训练数据极为稀缺。我们提出了一种基于神经符号规则的框架，用于对这些模型进行错误校正与检测，以支持最终在安全场景中的部署。我们针对多个最新及最先进的模型开展了一系列实验，结果表明：在所有类别均存在于训练集中的情况下，该方法相较于当前最优模型实现了1.7%的准确率提升；当训练集缺失40%的类别时，无需对基础模型进行重新训练，即可获得零样本下5.2%的改进及小样本下23.9%的提升。