@inproceedings{07fd40e7cefa419cb67b5d44210ea763,
title = "Modelling of Pedestrian Movements near an Amenity in Walkways of Public Buildings",
abstract = "Urban living experience is centered around public buildings where many pedestrians are engaging with services and functions provided in the form of amenities. Knowledge of interactions between pedestrians and amenities is critical to many applications involving robots. This paper presents a novel approach to modelling pedestrian movements around an amenity. A dual-level architecture consisting of a classifier of pedestrian intentions and specialized sub-models for pedestrian movements in the vicinity is proposed. The models exploit features of the amenities and the environment that are learned from pedestrian trajectories. A dataset of pedestrian movements around an information kiosk in a shopping mall has been used to build the models and evaluate the performance. The findings show that the model can effectively explain the interactions between pedestrians and an amenity through quantitative and qualitative analysis.",
keywords = "amenities, deep learning, engagement, pedestrian movements, pedestrian trajectory modelling, public buildings",
author = "Lui, {Andrew Kwok Fai} and Chan, {Yin Hei} and Leung, {Man Fai}",
note = "Funding Information: ACKNOWLEDGMENT The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS16/E12/20). Publisher Copyright: {\textcopyright} 2022 IEEE.; 8th International Conference on Control, Automation and Robotics, ICCAR 2022 ; Conference date: 08-04-2022 Through 10-04-2022",
year = "2022",
doi = "10.1109/ICCAR55106.2022.9782667",
language = "English",
series = "2022 8th International Conference on Control, Automation and Robotics, ICCAR 2022",
pages = "394--400",
booktitle = "2022 8th International Conference on Control, Automation and Robotics, ICCAR 2022",
}