Influence of architectural visual proportion in street landscapes on pedestrian emotions
Visual elements are the main information source for public perception of street scenes,and public perception evaluation and emotional value have a significant impact on street scene design.By using deep learning networks to segment images and quantify street scene elements,combined with eye movement and physiological indicators to measure pedestrian emotional representation,this study explores the potential impact of architectural visual proportion on pedestrian emotions.The results show that:changes in spatial richness in old street scene environments have no significant impact on pedestrian pleasure;the visual search process of pedestrians in street scene spaces is significantly related to the visual proportion of street buildings,and there are significant differences in visual preferences of pedestrians in street scenes with different architectural visual proportions;with changes in architectural visual proportion,the level of pedestrian emotional arousal shows dif-ferent patterns,and there is a range threshold for architectural visual proportion that provides psychological pleasure.By exploring the potential impact of street scene architecture on pedestrian psychology through a com-bination of subjective and objective methods,this study can provide design suggestions for urban street renewal from a human perspective.
architectural visual proportionstreet scene designpublic perceptionemotional valuephysiologi-cal indicatorseye tracking