基于图像分析的地铁车站标识视觉显著性影响因素研究

IF 2.1 3区 心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Mengya Yin , Xilin Zhou , Qunfeng Ji , Huairen Peng , Shize Yang , Chuancheng Li
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引用次数: 0

摘要

关于颜色、光线和标识位置对地下标识视觉显著性的影响,已经进行了许多研究。然而,室内视觉环境对行人标志显著性的影响研究较少。为了探索影响地铁车站标识视觉显著性的因素,我们开发了一种新的分析方法,将显著性和焦点图相结合。然后,采用问卷调查的方式统一显著性图和焦点图的不同格式的结果。利用所提出的方法在选定的地点探索了影响标识视觉显著性的因素,并通过虚拟现实实验进行了验证。此外,本研究提出了一种基于图像分析的方法,揭示了影响地铁站行人对标牌关注的多层次因素,包括空间界面、人群流量和环境光。结果表明,人群流量对行人注意标志的影响最大。研究结果强调了在火车站设计中考虑行人动态的重要性,这对于提供高质量的地铁体验至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image-analysis-based method for exploring factors influencing the visual saliency of signage in metro stations
Many studies have been conducted on the effects of color, light, and signage location on the visual saliency of underground signage. However, few studies have investigated the influence of indoor visual environments on the saliency of pedestrian signage. To explore the factors that influence the visual saliency of signage in metro stations, we developed a novel analysis method using a combination of saliency and focus maps. Then, questionnaires were utilized to unify the various formats of results from the saliency and focus maps. The factors that influence the visual saliency of signage were explored using the proposed method at selected sites and validated through virtual reality experiments. Additionally, this study proposes an image-analysis-based method that reveals the multilevel factors affecting pedestrian attention to signage in underground metro stations, including spatial interfaces, crowd flow, and ambient light. The results indicate that crowd flow has the greatest impact on pedestrian attention to signage. The study’s findings underscore the significance of considering pedestrian dynamics in the design of railway stations, which is crucial for delivering a high-quality subway experience.
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来源期刊
Cognitive Systems Research
Cognitive Systems Research 工程技术-计算机:人工智能
CiteScore
9.40
自引率
5.10%
发文量
40
审稿时长
>12 weeks
期刊介绍: Cognitive Systems Research is dedicated to the study of human-level cognition. As such, it welcomes papers which advance the understanding, design and applications of cognitive and intelligent systems, both natural and artificial. The journal brings together a broad community studying cognition in its many facets in vivo and in silico, across the developmental spectrum, focusing on individual capacities or on entire architectures. It aims to foster debate and integrate ideas, concepts, constructs, theories, models and techniques from across different disciplines and different perspectives on human-level cognition. The scope of interest includes the study of cognitive capacities and architectures - both brain-inspired and non-brain-inspired - and the application of cognitive systems to real-world problems as far as it offers insights relevant for the understanding of cognition. Cognitive Systems Research therefore welcomes mature and cutting-edge research approaching cognition from a systems-oriented perspective, both theoretical and empirically-informed, in the form of original manuscripts, short communications, opinion articles, systematic reviews, and topical survey articles from the fields of Cognitive Science (including Philosophy of Cognitive Science), Artificial Intelligence/Computer Science, Cognitive Robotics, Developmental Science, Psychology, and Neuroscience and Neuromorphic Engineering. Empirical studies will be considered if they are supplemented by theoretical analyses and contributions to theory development and/or computational modelling studies.
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