Continuous Road Health Monitoring – How Street View Videos with Computer Vision Improve Safety and Generate Savings for Cities and Road Authorities

2020 ◽  
Author(s):  
Markus Melander ◽  
Ilari Pihlajisto
Keyword(s):  
Computer Vision ◽  
Health Monitoring ◽  
Street View ◽  
Road Health Monitoring

Feature Engineering for Structural Health Monitoring (SHM)

2021 ◽  
pp. 138-163
Author(s):  
Esraa Elhariri ◽  
Nashwa El-Bendary ◽  
Shereen A. Taie
Keyword(s):  
Computer Vision ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Emerging Technology ◽  
The Other ◽  
Feature Engineering ◽  
Future Directions ◽  
Structural Health ◽  
Computational Perspective

Feature engineering is a key component contributing to the performance of the computer vision pipeline. It is fundamental to several computer vision tasks such as object recognition, image retrieval, and image segmentation. On the other hand, the emerging technology of structural health monitoring (SHM) paved the way for spotting continuous tracking of structural damage. Damage detection and severity recognition in the structural buildings and constructions are issues of great importance as the various types of damages represent an essential indicator of building and construction durability. In this chapter, the authors connect the feature engineering with SHM processes through illustrating the concept of SHM from a computational perspective, with a focus on various types of data and feature engineering methods as well as applications and open venues for further research. Challenges to be addressed and future directions of research are presented and an extensive survey of state-of-the-art studies is also included.

scholarly journals Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

2020 ◽  
Vol 17 (17) ◽  
pp. 6359 ◽  
Author(s):  
Quynh C. Nguyen ◽  
Yuru Huang ◽  
Abhinav Kumar ◽  
Haoshu Duan ◽  
Jessica M. Keralis ◽  
...  
Keyword(s):  
Computer Vision ◽  
Built Environment ◽  
Local Area ◽  
Physical Disorder ◽  
Single Family ◽  
Google Street View ◽  
Environment Effects ◽  
Street View ◽  
Sociodemographic Disparities ◽  
Family Home

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.

scholarly journals Google Street View-Derived Neighborhood Characteristics in California Associated with Coronary Heart Disease, Hypertension, Diabetes

2021 ◽  
Vol 18 (19) ◽  
pp. 10428
Author(s):  
Thu T. Nguyen ◽  
Quynh C. Nguyen ◽  
Anna D. Rubinsky ◽  
Tolga Tasdizen ◽  
Amir Hossein Nazem Deligani ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Computer Vision ◽  
Coronary Artery ◽  
Health Outcomes ◽  
San Francisco ◽  
Neighborhood Environment ◽  
Cross Sectional ◽  
Google Street View ◽  
Street View ◽  
Artery Disease

Characteristics of the neighborhood built environment influence health and health behavior. Google Street View (GSV) images may facilitate measures of the neighborhood environment that are meaningful, practical, and adaptable to any geographic boundary. We used GSV images and computer vision to characterize neighborhood environments (green streets, visible utility wires, and dilapidated buildings) and examined cross-sectional associations with chronic health outcomes among patients from the University of California, San Francisco Health system with outpatient visits from 2015 to 2017. Logistic regression models were adjusted for patient age, sex, marital status, race/ethnicity, insurance status, English as preferred language, assignment of a primary care provider, and neighborhood socioeconomic status of the census tract in which the patient resided. Among 214,163 patients residing in California, those living in communities in the highest tertile of green streets had 16–29% lower prevalence of coronary artery disease, hypertension, and diabetes compared to those living in communities in the lowest tertile. Conversely, a higher presence of visible utility wires overhead was associated with 10–26% more coronary artery disease and hypertension, and a higher presence of dilapidated buildings was associated with 12–20% greater prevalence of coronary artery disease, hypertension, and diabetes. GSV images and computer vision models can be used to understand contextual factors influencing patient health outcomes and inform structural and place-based interventions to promote population health.

Application of Computer Vision Technology to Structural Health Monitoring of Engineering Structures

2019 ◽  
pp. 256-268 ◽  
Author(s):  
X. W. Ye ◽  
T. Jin ◽  
P. Y. Chen
Keyword(s):  
Computer Vision ◽  
Health Monitoring ◽  
Electromagnetic Interference ◽  
High Speed ◽  
Low Noise ◽  
Bridge Inspection ◽  
Engineering Structures ◽  
Computer Vision Technology ◽  
Aerial Vehicle ◽  
Railway System

The computer vision technology has gained great advances and applied in a variety of industry fields. It has some unique advantages over the traditional technologies such as high speed, high accuracy, low noise, anti-electromagnetic interference, etc. In the last decade, the technology of computer vision has been widely employed in the field of structure health monitoring (SHM). Many specific hardware and algorithms have been developed to meet different kinds of monitoring demands. This chapter presents three application scenarios of computer vision technology for health monitoring of engineering structures, including bridge inspection and evaluation with unmanned aerial vehicle (UAV), recognition and surveillance of foreign object intrusion for railway system, and identification and tracking of concrete cracking. The principles and procedures of three application scenarios are addressed following with the experimental study, and the possibilities and ideas for the application of computer vision technology to other monitoring items are also discussed.

Use of Statistical Analysis, Computer Vision, and Reliability for Structural Health Monitoring

2010 ◽  
Author(s):  
F. Necati Catbas ◽  
Mustafa Gul ◽  
H. Burak Gokce ◽  
Taha Dumlupinar ◽  
Ricardo Zaurin
Keyword(s):  
Computer Vision ◽  
Statistical Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Health ◽  
Analysis Computer
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