вЂњThen, based on a new method that obviates the need for actual in-situ referencing we devised, weвЂ™re able to accurately measure the distance between people in the frame to see if they are too close to each other, or itвЂ™s just too crowded.вЂќ

вЂњThis allows us to identify what is in the frame to determine the relationship between the objects in that frame,вЂќ said Ozbay. The system also blurs out any identifying images such as faces, without impacting the effectiveness of the algorithm. They then used an off-the-shelf deep-learning image-processing algorithm to process each frame of the video to learn what each frame contains: a bus, a car, a pedestrian, a bicycle, etc. To do this, Ozbay and his team wrote a вЂњcrawlerвЂќ-essentially a tool to index the video content automatically-to capture the low-quality images from the video feeds available on the internet. вЂњOur idea was to take advantage of these DOT camera feeds and record them so we could better understand social distancing behavior of pedestrians,вЂќ said Kaan Ozbay, Director of C2SMART and Professor at NYU.

To accomplish this, C2SMART researchers leveraged publicly available New York City Department of Transportation (DOT) video feeds from the cover over 700 locations throughout New York City and applied a deep-learning, camera-based object detection method that enabled researchers to calculate pedestrian and traffic densities without ever needing to go out onto the streets. Researchers at New York UniversityвЂ™s (NYU) Connected Cities for Smart Mobility towards Accessible and Resilient Transportation (C2SMART) Center, a Tier 1 USDOT-funded University Transportation Center, developed a solution that not only eliminated the risk of infection to researchers, and which could easily be plugged into already existing public traffic camera feeds infrastructure, but also provided the most comprehensive data on crowd and traffic densities that had ever been compiled previously and cannot be easily detected by conventional traffic sensors. But sending teams of researchers out into the streets to observe and tabulate these numbers would have involved putting those researchers at risk of exposure to the very infection the strategies were meant to curb. In the midst of the COVID-19 pandemic, in 2020, many research groups sought an effective method to determine mobility patterns and crowd densities on the streets of major cities like New York City to give insight into the effectiveness of stay-at-home and social distancing strategies. This is a sponsored article brought to you by NYUвЂ™s Tandon School of Engineering.

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