How have people responded to COVID-19 restrictions around the world?

Website organizes public camera footage to study social distancing

WEST LAFAYETTE, Ind. — Public camera footage of how people have responded so far to COVID-19 social distancing guidelines in spaces such as tourist spots and busy street corners could help inform new policies as the pandemic progresses.

But that footage is scattered all over the internet.

Purdue University engineers built a website that pools together live footage and images from approximately 30,000 network cameras in more than 100 countries, making data easier to analyze.

The site has documented footage since March that could help evaluate the effectiveness of lockdowns and restrictions. Video and images captured by the system do not identify individuals – just the number of people in a public space from a distance. The system also does not use facial recognition technology.

Yung-Hsiang Lu and his students are using data from thousands of public network cameras worldwide to see how COVID-19 restrictions have affected crowd size and behavior over time. (Purdue University photo/John Underwood) Download image

Researchers and policymakers can visit the site cam2project.net to access the footage. The resource is described further in a paper pending publication and supported by a grant from the National Science Foundation

“Researchers already have the tools they need to analyze human behavior from video and photos, but this behavior can vary significantly depending on the context or culture of a place. We need extensive data to get those detailed insights, and this site provides that data,” said Yung-Hsiang Lu, a Purdue professor of Electrical and Computer Engineering

A video by Lu’s lab explaining this resource is available on YouTube

Images and footage from network cameras, such as those overlooking city streets and squares, are publicly available on the internet. But because each website organizes and presents visual data differently, it would be challenging and tedious to sift through each network camera’s feed.

COVID-19 lockdowns have made public spaces look drastically different. A new tool is helping researchers discern trends that could inform policies going forward. Photo: Times Square on June 23, 2019 (left) and March 1, 2020. (Source: EarthCam) Download image

The system that Lu’s team developed automatically discovers thousands of network cameras in public spaces. After the system discovers cameras, a computer program saves image data and downloads videos about every 10 minutes. Data recorded from these cameras are sent to cloud data centers for processing.

The project has been allocated computational power and storage provided by the Argonne Leadership Computing Facility Cooley cluster located at the U.S. Department of Energy’s Argonne National Laboratory.

The discovered cameras are a subset of a much larger system developed in Lu’s lab in 2016, called the Continuous Analysis of Many CAMeras (CAM²). The CAM² system is the world’s largest camera network, accessing more than 120,000 cameras worldwide in settings ranging from public parking garages to highways.

The cameras that Lu’s lab has discovered for studying the effects of COVID-19 restrictions focus on places typically dominated by pedestrians.

Lu and his collaborators have been using the system and artificial intelligence tools to see how policies have affected crowd size over time. The data also is helping to build models for human interactions and the spread of disease. Lu’s team received approval and protocol from the Institutional Review Board to conduct this study.

“How have people responded to policy changes? Were there sudden increases of crowds when the restrictions lifted, or were there gradual increases? Are there obvious patterns by countries or regions? These are the types of questions we hope to answer,” Lu said.

The system is based on several technologies protected by patents filed through the Purdue Research Foundation Office of Technology Commercialization

Co-PIs on this project include Purdue professors David Ebert, David Barbarash and Wei Zakharov. George Thiruvathukal, a professor at Loyola University Chicago and visiting professor at Argonne National Laboratory, is the project’s consultant for software development and data management.

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Writer: Kayla Wiles, wiles5@purdue.edu (working remotely, but will provide immediate response)

Source: Yung-Hsiang Lu, yunglu@purdue.edu

Note to Journalists: A video by the researchers explaining this project is available on YouTube. A copy of the paper is available for download via the Arxiv preprint server. A photo of Yung-Hsiang Lu and public camera images of Times Square are available via Google Drive

ABSTRACT

Observing Responses to the COVID-19 Pandemic using Worldwide Network Cameras

Isha Ghodgaonkar, Abhinav Goel, Fischer Bordwell, Caleb Tung, Sara Aghajanzadeh,

Noah Curran, Ryan Chen, Kaiwen Yu, Sneha Mahapatra, Vishnu Banna, Gore Kao,

Kate Lee, Xiao Hu, Nick Eliopolous, Akhil Chinnakotla, Damini Rijhwani, Ashley Kim,

Aditya Chakraborty, Mark Daniel Ward, Yung-Hsiang Lu, George K. Thiruvathukal*

Purdue University, West Lafayette, IN, USA

*Loyola University Chicago, IL, USA

COVID-19 has resulted in a worldwide pandemic, leading to “lockdown” policies and social distancing. The pandemic has profoundly changed the world. Traditional methods for observing these historical events are difficult because sending reporters to areas with many infected people can put the reporters’ lives in danger. New technologies are needed for safely observing responses to these policies. This paper reports using thousands of network cameras deployed worldwide for the purpose of witnessing activities in response to the policies. The network cameras can continuously provide real-time visual data (image and video) without human efforts. Thus, network cameras can be utilized to observe activities without risking the lives of reporters. This paper describes a project that uses network cameras to observe responses to governments’ policies during the COVID-19 pandemic (March to April in 2020). The project discovers over 30,000 network cameras deployed in 110 countries. A set of computer tools are created to collect visual data from network cameras continuously during the pandemic. This paper describes the methods to discover network cameras on the Internet, the methods to collect and manage data, and preliminary results of data analysis. This project can be the foundation for observing the possible “second wave” in fall 2020. The data may be used for post-pandemic analysis by sociologists, public health experts, and meteorologists.