Evidence map›Paper›PMID 40096144›Full record

ArticleSensors (Basel, Switzerland)2025

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

Jinjing Zhao, Yunfan Chen, Yancheng Li, Haotian Xu, Jingjing Xu, Xuliang Li, Hong Zhang, Lei Jin, Shengyong Xu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jinjing ZhaoSchool of Electronics, Peking University, Beijing 100871, China.ORCID 0009-0000-0462-867X
Yunfan ChenSchool of Electronics, Peking University, Beijing 100871, China.
Yancheng LiSchool of Electronics, Peking University, Beijing 100871, China.
Haotian XuSchool of Electronics, Peking University, Beijing 100871, China.
Jingjing XuSchool of Integrated Circuits, Shandong University, Jinan 250100, China.ORCID 0000-0002-0443-547X
Xuliang LiSchool of Aerospace, Beihang University, Beijing 102206, China.
Hong ZhangSchool of Aerospace, Beihang University, Beijing 102206, China.
Lei JinAlpheus Robotics Technology Co., Ltd., Wuxi 214117, China.
Shengyong XuSchool of Electronics, Peking University, Beijing 100871, China.ORCID 0000-0001-9567-5980

Funding

National Key R&D Program of China no. 2017YFA0701302
6 · The paper itself

Abstract

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.

Indexed as

computer visiongeolocationlandmarkspedestrian-centric applicationremote monitoringurban spatial perceptionvisualization

Identifiers

PMID40096144
PMCPMC11902832

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.