The recent breakout of coronavirus disease (COVID) has been requiring social distancing to mitigate its spread. Social distancing is a simple-but-effective way to reduce the human-to-human transmission of this deadly virus. A distance of at least 1.5 m between any two nearby people is typically required in many places. However, this social distancing requirement is not always respected, especially in space-confined places. Naturally, it is desired to have an autonomous system that can automatically detect the distance between nearby humans and warn people when the distance requirement is violated. To this end, the human positions are often required to be estimated. Thus, positioning and localization are important techniques to facilitate and enforce the social distancing requirement.

Many technological applications have been developed and implemented in the last two years to fight the COVID-19 pandemic via social distancing. Despite its importance in response to the coronavirus, the remaining challenge is the limitation of human resources to monitor and provide timely warnings to maintain appropriate activities, such as keeping distance between each other, wearing face masks, or complying with curfew restrictions. To tackle this problem, computer vision researchers have proposed numerous approaches for autonomous object detection and distance measurement, which will be summarised in this chapter. First, vision-based applications in intelligent surveillance systems for social distancing monitoring as well as masked face detection, are introduced. Then, core classical and modern neural network-based methodologies for these applications are analysed. A simple masked face detection is developed to verify its effectiveness and limitations, followed up by remarks and discussions on open problems.

In the context of social distancing, various sensing and automation technologies can bring key roles in providing intelligent social distancing scenarios. In this chapter, we present overviews and state-of-the-art applications of emerging sensing and automation technologies including ultrasound, inertial sensor, visible light, and thermal. These four technologies can provide small-to-large coverage, sufficiently low deployment and operational costs, and high accuracy as well as privacy for indoor and/or outdoor environments. Specifically, ultrasound technology that leverages periodical ultrasonic beacons (UBs) can be utilized to keep distance among people, real-time monitoring in public buildings, and mobile robot navigation in indoor environments. Inertial sensor technology, which contains a gyroscope and accelerometer, is useful to recognize positions of pedestrians for keeping distance and perform automation using autonomous vehicles, e.g., medical robots and unmanned aerial vehicles (UAVs). The use of visible light coming from the light-emitting diodes (LEDs) can also provide low-cost crowd monitoring system as well as a navigation assistance system in a large-scale indoor area, and smart traffic control among vehicles on the roads. In a low-light condition, thermal-based system using infrared and imaging camera can be used to control distance among people, physical contact tracing, and real-time traffic monitoring over long distances. To this end, each emerging sensing/automation technology has unique features that are expected to be the potential solution for specific social distancing scenarios. In the following sections, we discuss the aforementioned sensing/automation technologies and their scenarios for social distancing applications in more detail.

Optimization theories can offer unprecedented improvements in providing satisfactory quality-of-services to all people while controlling the number of people and their positions at a predetermined level in a place inside the context of social distancing. Various academic researches in the literature have employed optimization theories to design efficient algorithms under practical scenarios comprising physical space, healthcare, workforce, traffic, online service management and computer-based distance monitoring. This chapter focuses on two specific scheduling subjects: People scheduling and traffic scheduling. People scheduling can effectively exploit available space within particular workplaces or essential public centers to minimize physical contact, prevent virus spread and increase the number of people in space while still ensuring a certain safety level. Traffic scheduling can assist in reducing, for example, the peak number of pedestrians and vehicles. Furthermore, network resource optimization can help the growing demands on online services since the remote working mode has aggressively increased during the COVID-19 pandemic. We present several open issues and draw potential future research directions in the social distancing context by the optimization applications to assess fundamental properties and system performance.

Vaccination is considered as the most effective solution to fight against the COVID-19 epidemic as well as other contagious and infectious diseases to bring the world to a "new normal" lifestyle. This lifestyle is defined as a new way of living our work, routines, and interactions with other people to adapt with COVID-19. With the ambition to open up the economy, many countries such as United Arab Emirates, Portugal, and Singapore have achieved the coverage rate of COVID-19 vaccines for the 2nd dose above 80%. However, when the vaccine has not been evenly distributed to all countries worldwide, it means that COVID-19 cannot be ended. This is because fully vaccinated people can still be positive with COVID-19, and the effectiveness of the vaccine also decreases significantly after 6 months. Therefore, protective measures like social distancing, wearing mask, and frequent handwashing must also be practiced simultaneously to enable the "new normal" lifestyle. In this chapter, we discuss the open issues of social distancing implementation such as pandemic mode, hybrid technology solutions, security and privacy concerns, social distancing encouragement, real-time scheduling, and negative effects. Furthermore, potential solutions to these issues are also discussed.