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.

利用基于规则的神经符号框架，改进了对运动轨迹进行分类的任务，用于支持安全应用部署，实验证明与目前最先进模型相比，其在训练时所有类别都出现的情况下准确率提高了 1.7％，在 40％的类别被省略的情况下，零样本和几样本情况下其准确率分别提高了 5.2％和 23.9％，而无需重新训练基础模型。

基于规则的错误检测和修正，实现运动轨迹分类

Rule-Based Error Detection and Correction to Operationalize Movement  Trajectory Classification

National Basketball Association (NBA) players are highly motivated and
skilled experts that solve complex decision making problems at every time point
during a game. As a step towards understanding how players make their
decisions, we focus on their movement trajectories during games. We propose a
method that captures the multi-modal behavior of players, where they might
consider multiple trajectories and select the most advantageous one. The method
is built on an LSTM-based architecture predicting multiple trajectories and
their probabilities, trained by a multi-modal loss function that updates the
best trajectories. Experiments on large, fine-grained NBA tracking data show
that the proposed method outperforms the state-of-the-art. In addition, the
results indicate that the approach generates more realistic trajectories and
that it can learn individual playing styles of specific players.

研究了国家篮球协会（NBA）球员在比赛过程中复杂决策问题的解决方法，提出了一种基于 LSTM 架构的方法来预测运动轨迹和其概率，并通过多模式损失函数更新最佳轨迹，实验结果表明该方法胜过现有技术且能够学习到球员的个人风格。