Leslie Gibson Mccarthy at Medical Xpress reports on a study out of Washington University in St. Louis published in the American Journal of Preventive Medicine entitled “Emerging Technologies: Webcams and Crowd-Sourcing to Identify Active Transportation.”
The goal of the study was to measure the use of man-made spaces like parks, trails and bike paths to better inform public health policy and promote a more physically active, less obese citizenry.
According to study co-author J. Aaron Hipp, PhD, assistant professor of public health at the Brown School, findings suggest the use of webcams and crowdsourcing can assist urban planners to design public spaces that foster more physical movement. In addition, computer vision experts can help improve public safety by incorporating machine learning as well.
The study used publicly available outdoor webcams and crowdsourcing to count people, bikes and cars. These two approaches helped overcome common obstacles to getting accurate counts such as rain, fog and crowded conditions.
For the webcam imagery, researchers relied on the web tool, AMOS (Archive of Many Outdoor Scenes) developed by study co-author Robert Pless, PhD, professor in the WUSTL School of Engineering & Applied Science. AMOS crawls for publicly available outdoor webcams and timestamps one image per camera per 30 minute interval.
Using the Amazon Mechanical Turk website, crowdsourced workers marked the pedestrians, bicycles and vehicles in images captured over a year in which a bike lane was installed at an intersection in Washington, DC.
What was the impact of installing the bike lane in this area? Cycling quadrupled.
Ubiquitous webcams and crowdsourced data enable researchers to pinpoint how changes in environment, design, and policy affect people’s actual usage of public amenities. As such, communities can better allocate infrastructure budgets in service of public health goals.
Perhaps, over time, it’s possible we’ll see computer vision and machine learning pick up more and more of what gets crowdsourced today.
With this in mind, how do you see webcams, crowdsourcing and computer vision serving your needs?