  In order to contain the COVID-19 pandemic, countries around the world have
introduced social distancing guidelines as public health interventions to
reduce the spread of the disease. However, monitoring the efficacy of these
guidelines at a large scale (nationwide or worldwide) is difficult. To make
matters worse, traditional observational methods such as in-person reporting is
dangerous because observers may risk infection. A better solution is to observe
activities through network cameras; this approach is scalable and observers can
stay in safe locations. This research team has created methods that can
discover thousands of network cameras worldwide, retrieve data from the
cameras, analyze the data, and report the sizes of crowds as different
countries issued and lifted restrictions (also called ''lockdown''). We
discover 11,140 network cameras that provide real-time data and we present the
results across 15 countries. We collect data from these cameras beginning April
2020 at approximately 0.5TB per week. After analyzing 10,424,459 images from
still image cameras and frames extracted periodically from video, the data
reveals that the residents in some countries exhibited more activity (judged by
numbers of people and vehicles) after the restrictions were lifted. In other
countries, the amounts of activities showed no obvious changes during the
restrictions and after the restrictions were lifted. The data further reveals
whether people stay ''social distancing'', at least 6 feet apart. This study
discerns whether social distancing is being followed in several types of
locations and geographical locations worldwide and serve as an early indicator
whether another wave of infections is likely to occur soon.
