In the midst of this pandemic, lockdown and lockdown and proper physical distancing appear to be the best possible prevention mechanism. In this paper, an attempt is made towards building a software system that ensures monitoring of the same in relatively much smaller and sufficiently closed system like a college campus.
The COVID-19 pandemic has forced upon a lot of sudden changes across our society. Today, for the sake of those who are yielding to the severe outcomes of the corona-virus, we need to be more considerate by maintaining proper lockdown protocols — maintaining proper physical distancing, without at the same time emotionally distancing ourselves from others.
In this paper, I try to identify the major aspects of lockdown as a prevention mechanism and provide suitable software solutions to beneficiate the same.
As mentioned above, there are several aspects to a lockdown that ensures safety of individuals at large.
Thus, this brings us to our problem statement. How to make a efficient platform that solves all the four above issues, as stated before? This is what I try to approach towards in this paper.
Better image processing tools and effectiveness of computer vision systems have resulted in a paramount progress in the area of face detection. This is what we have seen being used in several research work towards the current problem of face mask detection.
SSDMNV2 is a top-notch system addressing the face mask detection problem using a Single Shot Multi-box Detector which detects faces that are classified using MobilenetV2 architecture. As reported, this system is said to have a astonishing accuracy score of $0.9264$.
Multi-Resolution Crowd Network serves as a lucrative approach for the crowd counting problem. It relies on a pre-trained encoder which transmits data into an up-sampling layer and further into a series of convolution layers or any other suitable combination of both — which behaves as a decoder.
This system is quite robust as serves as one of the current top edge systems for crowd counting in the industry. Furthermore, it is extensible to result in density map estimation which we effectively require for management of social distancing.
Learnable density map representations are quite resourceful. These can be trained alongside systems that yield density map estimators, as discussed above. As seen from the Wan et. al., we can conclude that such map generation, with merely dot map as input and an associated state of the art performance, would help us a long way in crowd management, warning and overall ensuring the effectiveness of proper lockdown protocols.
Drones can indeed perform a crucial role in the field of constructing safe delivery systems. In those terms, there has been two major ways that drones have been implemented for delivery.
The first one involves supply of food and other essential items while the second one mainly deals with the supply of health-kits and medical products. We have a lot of innovations in either direction during this COVID-19 era. Companies like Zipline and studies like that of Poljak et. al. have shown the feasibility and efficiency of using such a system.
The proposed Lockdown Monitoring System (LMD) shall have three major sections.
The major features shall be the use cases obtained from the above structure. We can visualize the system as an application where you put in required data regarding yourself, namely — Health Condition, GPS location along with primary information like Name, Age, etc.
Then, you shall have a live view of population density across all major locations within the campus and an estimate of infection threat for every crowd or group of people.
An additional service shall be provided in the form of booking drone deliveries from any person (who might not might not be representing a group, for example, canteen or mess) to another — as long as both of them are users of the application.
Moreover, based on the density data we shall be able to provide the college administration with statistics regarding crowd distribution and flow of infection. The institute, as a whole, shall be better informed through this for undertaking required changes or restrictions to control crowding and reduce infection transmission.
The system shall have one main databases (for Users) and a derived database (for Crowds).
Major relations and properties have been presented in my UML and Class Diagram.
Before delving into the architecture of the system, let's talk about the requirements.
In conclusion, this system shall serve as a one-stop platform that ensures ease of taking precautions, managing health, as well as maintaining a effective lockdown protocol within the college campus ecosystem. However, for a proper implementation of such a system — extensive help shall be required both on behalf of the institute as well as the residents especially through their willingness.