4D+ City Sidewalk: Integrating Pedestrian View into Sidewalk Spaces to Support User-Centric Urban Spatial Perception

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 5; p. 1375
• Main Authors: Zhao, Jinjing, Chen, Yunfan, Li, Yancheng, Xu, Haotian, Xu, Jingjing, Li, Xuliang, Zhang, Hong, Jin, Lei, Xu, Shengyong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.02.2025; MDPI
• Subjects: Cameras; Computer vision; geolocation; Global positioning systems; GPS; Infrastructure; Internet of Things; landmarks; Localization; Mobility; pedestrian-centric application; Pedestrians; remote monitoring; Researchers; Semantics; Sensors; Smart cities; Traffic accidents & safety; Urban areas; Urban planning; urban spatial perception; Vehicles; Visualization; pedestrian-centric application; visualization; computer vision; remote monitoring; landmarks; urban spatial perception; geolocation
• DOI: 10.3390/s25051375
• ISSN: 1424-8220

As urban environments become increasingly interconnected, the demand for precise and efficient pedestrian solutions in digitalized smart cities has grown significantly. This study introduces a scalable spatial visualization system designed to enhance interactions between individuals and the street in outdoor sidewalk environments. The system operates in two main phases: the spatial prior phase and the target localization phase. In the spatial prior phase, the system captures the user’s perspective using first-person visual data and leverages landmark elements within the sidewalk environment to localize the user’s camera. In the target localization phase, the system detects surrounding objects, such as pedestrians or cyclists, using high-angle closed-circuit television (CCTV) cameras. The system was deployed in a real-world sidewalk environment at an intersection on a university campus. By combining user location data with CCTV observations, a 4D+ virtual monitoring system was developed to present a spatiotemporal visualization of the mobile participants within the user’s surrounding sidewalk space. Experimental results show that the landmark-based localization method achieves a planar positioning error of 0.468 m and a height error of 0.120 m on average. With the assistance of CCTV cameras, the localization of other targets maintains an overall error of 0.24 m. This system establishes the spatial relationship between pedestrians and the street by integrating detailed sidewalk views, with promising applications for pedestrian navigation and the potential to enhance pedestrian-friendly urban ecosystems.