A Smart Social Distance Monitoring System

The entire world is affected by Covid-19 pandemic. One of the major reasons is that it is contagious and a minimum distance should be maintained to stay safe. Social distancing might be a difficult task to implement effectively. Social distancing plays a pivotal role in curbing diseases that are contagious like Covid-19.Now that situations are returning to normal, the risk of getting infected is still high. Governments are deciding to ease lockdown regulations, as part of the unlocking public places, workspaces and educational institutions started to resume their activities. Considering the current scenario, the public has to strictly follow all the necessary Covid-19 protocols to reduce the spike in the number of Covid cases. This project aims to develop a prototype device that helps in implementing social distancing using Ultra-Wide Band (UWB) wireless technology based solution. Prototype issues an alert signal when the distance between individuals is less than the prescribed threshold distance. If the protocol is breached, the user is alarmed through an LED. UWB is known for its advantages as it has greater signal strength compared to Bluetooth. The design of the prototype enables implementation as wearable such as an ID card. © 2022 IEEE.

Surveillance Rover to Maintain Social Distancing in Crowded Areas

COVID-19 is making a huge impact both in terms of the economy and human lives. Many lost their lives due to COVID-19 which is found in most of the nations. The number of positive symptoms is increasing rapidly all over the world. To safeguard us from the virus, some protocols have been addressed by WHO in which people has to wear a mask and make a social distancing when moved in public. Therefore, social distancing places an important role in preventing us from the spread of the diseases. The minimum distance between to be maintained is informed at 6 feet informed by the health organizations. When people gathered on a group social distancing could not be maintained even if manual or any kind of technology implemented. Temperature measurement on mass gathering was also a tedious process where the monitoring is essential. Multiple methods such as thermal cameras, temperature sensors for monitoring the personnel has not been efficient. In the proposed work to monitor the social distancing between the persons an ultrasonic sensor is placed to detect the obstacle and an IR sensor to make the rover move. An encoder is used to calculate the distance based on the rpm of the wheel. Based on this input the distance is checked within this limit the obstacle is detected, an alert signal is made using the buzzer. A thermal sensor is used to measure the temperature of the person and an LCD display shows the temperature of the person and distance between obstacles. The proposed system has resulted in identifying the distance and helps in reducing the spread during the pandemic situation. © 2023 IEEE.

Proposing Algorithm for Warning Minimum Distance Violation and Tracking Covid-19 Object Using YOLOV4 Model

Currently, the prevention of the spread of the Covid-19 epidemic is still a matter of concern with many new variants that are more infectious and making it more difficult to prevent it. In addition, several respiratory viral diseases such as influenza A, monkeypox, etc. help promote the management and prevention of epidemics. The paper presents the system using the YOLOV4 object recognition model to identify human objects from videos extracted. To increase accuracy with the desired context, we build a dataset of people and perform training on them. We use the Euclidean algorithm to calculate the distance between bounding box pairs. We then use a physical distance that approximates the pixel and set a threshold. It is possible to determine who has violated the minimum social distance threshold. In addition, we apply a tracking algorithm to be able to detect and trace those who have been in close contact with the cases to be monitored. The system has been performed on video and the accuracy of the model is up to 95.6%. © 2022 IEEE.

Social Distancing Violation Detection in Video Using ChessBoard and Bird's-eye Perspective

Thousands of infections, hundreds of deaths every day - these are numbers that speak the current serious status, numbers that each of us is no longer unfamiliar with in the current context, the context of the raging epidemic - Coronavirus disease epidemic. Therefore, we need solutions and technologies to fight the epidemic promptly and quickly to prevent or reduce the effect of the epidemic. Numerous studies have warned that if we contact an infected person within a distance of fewer than two meters, it can be considered a high risk of infecting Coronavirus. To detect a contact distance shorter than two meters and provides warnings to violations in monitoring systems based on a camera, we present an approach to solving two problems, including detecting objects - here are humans and calculating the distance between objects using Chessboard and bird's eye perspective. We have leveraged the pre-trained InceptionV2 model, a famous convolutional neural network for object detection, to detect people in the video. Also, we propose to use a perspective transformation algorithm for the distance calculation converting pixels from the camera perspective to a bird's eye view. Then, we choose the minimum distance from the distance in the determined field to the distance in pixels and calculate the distance violation based on the bird's eye view, with camera calibration and minimum distance selection process based on field distance. The proposed method is tested in some scenarios to provide warnings of social distancing violations. The work is expected to generate a safe area providing warnings to protect employees in administrative environments with a high risk of contacting numerous people. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

SCORER-Gap: Sequentially Correlated Rules for Event Recommendation Considering Gap Size

Finding significant events, which follow a specific pattern, is an essential task in sequential rule mining. While the significance of a rule often is based on conditions like a maximum amount of time [1], or a minimum distance between patterns [2], the area between these two extremes is rarely analyzed. This paper aims at the discovery of partially-ordered sequential rules which satisfy a given correlation gap constraint. Applying this constraint to the support threshold determines a more relevant rule, among other parameters. We also require it in sparse datasets, where long sequences with many distinct events exist. This setting can be found in online product configurators, where the basis is an unstructured process that combines both high-level and fine-grained configuration steps. In general, our novel approach SCORER-Gap can be applied to procedures with a high variability of events.By focusing on the gap size between antecedent and consequent of a rule, we show that usually, the resulting vast number of rules gets highly reduced while keeping the flexibility between a minimum and a maximum distance in between. To implement our novel approach, we use an in-mining setup, namely RuleGrowth [1] to which we attach the correlation gap constraint as mentioned above. The code is available on [3]. For an extensive analysis of application areas, we use three real-world datasets consisting of different characteristics. We start with a Covid19 genome sequence representing a highly dense dataset. Additionally, an industrial database and the clickstream of a Hungarian news website (Kosarak) are used as representatives for increasingly sparse datasets.SCORER-Gap shows a high percentual reduction in the number of rules in the resulting ruleset while slightly increasing accuracy in a train and test setting. Furthermore, a high proportion of recommendation rules differs between RuleGrowth and SCORER-Gap. © 2021 IEEE.

Monitor Social Distance Using Convolutional Neural Network and Image Transformation

The COVID-19 pandemic has claimed millions of lives worldwide. In these times, the only sure-shot way to stay safe is to avoid social contact and to follow social distancing regulations. These regulations define the minimum distance people should keep from each other so as to avoid the propagation of the Coronavirus. Hence, monitoring the social distance between people becomes a real-world problem so as to ensure safety for everyone. This is especially difficult to do manually in public places. Our proposed system aims to allow an easy and effective way to measure social distance and identify the people at risk using Convolutional Neural Networks and Image Transformation techniques. © 2021 IEEE.

Alleviation of COVID by means of Social Distancing & Face Mask Detection Using YOLO V4

This paper consists of social distancing & face mask detection for the events of coronavirus, alleviation in such pandemic can be solved by social distancing as well as putting on a face mask. This small step of wearing a face mask as well as following social distancing would save lots of lives as the spread of the virus could be mitigated. YOLO stands for You Only Look Once, this algorithm is used for Object Detection as well as Object Tracking, this research uses YOLO for calculating the social distancing & identifying face mask on people’s face with the help of Object Detection, whereas tracking the face is done by Object Tracking. The minimum distance to keep while adhering for social distancing is 6 Feet, keeping this as the base for calculating distance, the model was trained and used for object detection as well as for object tracking. There are different types of algorithms available, YOLO stands out from all the other present currently. The custom datasets were used for the understanding the face masks and it was trained on those datasets for detection and tracking. For evaluation of the trained model, mAP (Mean Average Precision) was calculated for both the use cases (Social Distancing & Face Mask Detection), it works by comparing the ground-truth bounding box vs the detected box and, in the end, returns the score. The higher the mAP score would be, the better model is in the detection of objects. Mean Average Precision was calculated for two different thresholds (0.25 % & 0.50 %) with 101 recall points. Three different classes were created for classification those were Good, Bad & None, for which True Positive & False Positive values were calculated with ROC Curve for better understanding. © 2021 IEEE.