# DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
## 2 Related Works
### AI-Based Research  
The utilisation of Artificial Intelligence, Computer Vision, and Machine Learning, can help to discover the correlation of high-level features. For example, it may enable us to understand and predict pedestrian behaviours in traffic scenes, sports activities, medical imaging, or anomaly detection, by analysing spatio-temporal visual information and statistical data analysis of the images sequences [13; 19]. Among AI-Health related works, some researchers have tried to predict the sickness trend of specific areas [33], to develop crowd counting and density estimation methodologies in public places [34], or to determine the distance of individuals from the popular swarms [35] using a combination of visual and geo-location cellular information. However, such research works suffer from challenges such as skilled labour or the cost of designing and implementing the infrastructures. On the other hand, recent advances in Computer Vision, Deep Learning, and pattern recognition, as the sub-categories of the AI, enable the computers to understand and interpret the visual data from digital images or videos. It also allows computers to identify and classify different types of objects [36; 37; 38]. Such capabilities can play an important role in empowering, encouraging, and performing social distancing surveillance and measurements as well. For example, Computer Vision could turn CCTV cameras in the current infrastructure capacity into \"smart\" cameras that not only monitor people but can also determine whether people follow the social distancing guidelines or not. Such systems require very precise human detection algorithms. People detection in image sequences is one of the most important sub-branches in the field of object detection and computer vision. Although many research works have been done in human detection [39] and human action recognition [40], the majority of them are either limited to indoor applications or suffer from accuracy issues under outdoor challenging lighting conditions. A range of other research works rely on manual tuning methodologies to identify people activities, however, limited functionality has always been an issue [41]. Convolutional Neural Networks (CNNs) have played a very important role in feature extraction and complex object classification, including human detection. With the development of faster CPUs, GPUs, and extended memory capacities, CNNs allow the researchers to make accurate and fast detectors compared to conventional models. However, the long time training, detection speed and achieving better accuracy, are still remaining challenges to be solved. [PERSON] et al. [15] used a deep neural network (DNN) based detector, along with Deepsort [42] algorithm as an object tracker for people detection to assess the distance violation index--the ratio of number of people who violated the social distancing measure to the total number of the assessed group. However, no statistical analysis of the outcome of their results is provided. Furthermore, no discussion about the validity of the distance measurements is provided. In another study by [PERSON] et al. [43], the authors have addressed the people distancing in a given manufactory. They have used MobileNet V2 network [44] as a lightweight detector to reduce computational costs, which in turn provides less accuracy comparing to some other common models. Furthermore, the method only focuses on an indoor manufactory-setup distance measurement and does not provide any statistical assessment on the virus spread. Similar to the other research, no statistical analysis is performed on the results of the distance measurement in [45]. The authors have made a comparison between two common types of DNN models (You Only Look Once--YOLO and Faster RCNN ). However, the system accuracy has been only estimated based on a shallow comparison on different datasets with non-comparable ground truths.