School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, China.
Sensors (Basel). 2023 May 18;23(10):4882. doi: 10.3390/s23104882.
Functional objects are large and small physical entities installed in urban environments to offer specific functionalities to visitors, such as shops, escalators, and information kiosks. Instances of the novel notion are focal points of human activities and are significant in pedestrian movement. Pedestrian trajectory modelling in an urban scene is a challenging problem because of the complex patterns resulting from social interactions of the crowds and the diverse relation between pedestrians and functional objects. Many data-driven methods have been proposed to explain the complex movements in urban scenes. However, the methods considering functional objects in their formulation are rare. This study aims to reduce the knowledge gap by demonstrating the importance of pedestrian-object relations in the modelling task. The proposed modelling method, called pedestrian-object relation guided trajectory prediction (PORTP), uses a dual-layer architecture that includes a predictor of pedestrian-object relation and a series of relation-specific specialized pedestrian trajectory prediction models. The experiment findings indicate that the inclusion of pedestrian-object relation results in more accurate predictions. This study provides an empirical foundation for the novel notion and a strong baseline for future work on this topic.
功能物体是安装在城市环境中的大型和小型物理实体，为游客提供特定功能，如商店、自动扶梯和信息亭。这种新颖概念的实例是人类活动的焦点，在行人运动中具有重要意义。由于人群的社会互动和行人和功能物体之间的多样关系产生的复杂模式，城市场景中的行人轨迹建模是一个具有挑战性的问题。已经提出了许多数据驱动的方法来解释城市场景中的复杂运动。然而，在其公式中考虑功能物体的方法很少。本研究旨在通过证明在建模任务中行人-物体关系的重要性来缩小知识差距。所提出的建模方法称为行人-物体关系引导轨迹预测（PORTP），它使用双层架构，包括行人-物体关系预测器和一系列特定于关系的专业行人轨迹预测模型。实验结果表明，包含行人-物体关系可以实现更准确的预测。本研究为这一新颖概念提供了实证基础，并为该主题的未来工作提供了一个强有力的基准。
