Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a new approach for context reasoning to further advance the field of human motion prediction. We therefore propose a neuro-symbolic approach for human motion prediction (NeuroSyM), which weights differently the interactions in the neighbourhood by leveraging an intuitive technique for spatial representation called Qualitative Trajectory Calculus (QTC). The proposed approach is experimentally tested on medium and long term time horizons using two architectures from the state of art, one of which is a baseline for human motion prediction and the other is a baseline for generic multivariate time-series prediction. Six datasets of challenging crowded scenarios, collected from both fixed and mobile cameras, were used for testing. Experimental results show that the NeuroSyM approach outperforms in most cases the baseline architectures in terms of prediction accuracy.

翻译：对人类行为背景的推理对于许多实际应用至关重要，尤其是那些部署自主系统（如机器人）的应用。本文提出了一种新的背景推理方法，以进一步推进人体运动预测领域。为此，我们提出了一种用于人体运动预测的神经符号方法（NeuroSyM），该方法通过利用一种名为定性轨迹微积分（Qualitative Trajectory Calculus, QTC）的空间表示直觉技术，对邻域内不同交互给予差异化权重。该提出的方法在中期和长期时间跨度上，使用两种当前最先进的架构进行了实验测试，其中一种作为人体运动预测的基线，另一种作为通用多变量时间序列预测的基线。测试使用了六个来自固定和移动摄像头的具有挑战性的拥挤场景数据集。实验结果表明，NeuroSyM方法在大多数情况下在预测精度上优于基线架构。