Deep Crowd Analysis to Spot Social Distancing Violations in Post-COVID 19 Lifestyle

Crowd analysis is a new field of study that involves processing a large group of people to examine one or more of their behaviors. Deep learning is an appropriate technique for crowd analysis using a convolutional neural network. To calculate the distance between crowd members and to identify social distance violations, a deep crowd analysis is proposed in this study. Pre-trained in a single class To discover the region of interest, CNN is utilised to classify people (RoI). The people in the picture are then localized using a density map. The reference point used to calculate the distance between the people is the centroid of the isolated areas in the density map. A social distance violation is reported if the estimated distance is less than the specified threshold distance (3 meters). Between the two ROIs, a distance measured in pixels is determined. © 2022 IEEE.

Estimating social distance in public places for COVID-19 protocol using region CNN

The coronavirus disease has spread throughout the world and its fear has made people to be more cautious in public places. Since precautionary measures are the only reliable protocol to defend ourselves, social distancing is the only best approach to defend against the pandemic situation. The reproduction number i.e. R0 factor of COVID-19, can be slowed down only through the physical distancing norms. This research proposes a deep learning approach for maintaining the social distance by tracking and detecting the people present indoor and outdoor scenarios. Surveillance video is taken as the input and applied into you only look once (YOLO) V3 algorithm. The persons in the video are identified based on the segmentation algorithm present within the framework and then using Euclidean distance the image is evaluated. The bounding box algorithm helps to segregate the humans based on the minimum distance threshold. The proposed method is evaluated for images with peoples in the market, availing essential commodities and students entry inside a campus. Our proposed region-based convolutional neural network (RCNN) algorithm gives a better accuracy over the traditional models and hence the service can be implemented in general for places where social distancing is mandatory. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Behaviour Analysis of Izmir Residents Using Public Wi-Fi Access Point Usage

The wide distribution of access points in Izmir allows the collected information to be employed in smart city algorithms. In this study, we analyze the information that has been made publicly available by Izmir Metropolitan Municipality. We first show that the data is reliable, then analyze it from the perspectives of holidays, seasonal trends, and the COVID-19 pandemic. The study also shows that the information can be used for crowd analysis and forecasting, using K-means and SARIMA algorithms, respectively. © 2022 IEEE.

Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning

Coronavirus disease has a crisis with high spread throughout the world during the COVID19 pandemic period. This disease can be easily spread to a group of people and increase the spread. Since it is a worldly disease and not plenty of vaccines available, social distancing is the only best approach to defend against the pandemic situation. All the affected countries' governments declared locked-down to implement social distancing. This social separation and persons not being in a mass group can slow down the spread of COVID19. It reduces the physical contact between infected persons and normal healthy persons. Almost every health organization tells that to follow social distancing people should maintain at least 6 feet of distance from each other. This research proposes a deep learning approach for social distancing which is developed for tracking and detecting people who are in indoor as well as outdoor scenarios using YOLO V3 video analytic technique. This approach focuses to inspect whether the people are maintaining social distancing in many areas, using surveillance video with measuring the distance in real-time performance. Most of the early studies of detecting social distance monitoring were based on GPS for tracking the movements of people where the signals could be lost. On the other hand, some countries use drones to detect large gatherings of people who cannot have a clear view at night times [10]. In the future, the proposed system can be used fully for detecting threats in the public crowded or it can detect any person affected by critical situations (ie fainting, Cordia arrest) or planting the crops in the forms evenly with a uniform measurement. This proposal could be used in many fields like crowd analysis, autonomous vehicles, and human action recognition and could help the government authorities to redesign the public place layout and take precautionary action in the risk zones. This system analyses the social distancing of people by calculating the distance between people to slow downing the spread of the COVID 19 virus. © 2022 IEEE.

Diffusion Adaptation for Crowd Analysis

In this paper we develop a diffusion adaptation network to model the crowd movement and investigate the cooperation and self-organization ability of the people as network agents when they move towards the same target while facing the variation in space and arbitrary static obstacles. The crowd model is considered as a network of connected agents with adaptive abilities including some flexibilities in their movement speed and distances to battle a pandemic situation. The pathway varies in width and the agents adapt their distances and speeds to safely reach to their target. The model effectiveness is examined with respect to the changes in real environment parameters.

Object Detection for COVID Rules Response and Crowd Analysis

Understanding the hotspots attracting massive crowds is a huge necessity during this pandemic times. The knowledge of analyzing crowds will help to plan and avoid the spread of the virus to a large extent by identifying exact hotspots. Understanding where the crowds flock and whether they are following the guidelines or not will help in taking appropriate actions, allotting concerned personnel in advance, and closing of areas which are at higher risks can be advantageous. In order to realize the situation, real-time analysis of the pandemic rules like social distancing, wearing masks is necessary. This paper proposes the use of video surveillance and provides a combined application to check the factors necessary during crowd situations as per rules set by the Government. This work uses python as a coding language, and YOLOv4 algorithm along with various libraries like darknet to improve video and image analysis for the identification of exact requirements. This work also uses Cuda software and Cudnn library for the acceleration of processing. The paper proposes importantly, counting people passing through a particular area, detecting whether people are following social distancing, detecting if the participants are wearing a mask, and counting the number of vehicles passing through an area. The knowledge of analyzing crowds will help to plan and avoid the spread of the virus to a large extent by identifying exact hotspots. All the applications are connected to the graphical user interface (GUI) and depending on the input each application proposed considers different analysis. The types of input are image, video, image directory and live feed are considered to obtain better results. © 2021 IEEE.

Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks. Video surveillance and crowd management using video analysis techniques have significantly impacted today's research, and numerous applications have been developed in this domain. This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis. Managing the Kaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic. The Umrah videos are analyzed, and a system is devised that can track and monitor the crowd flow in Kaaba. The crowd in these videos is sparse due to the pandemic, and we have developed a technique to track the maximum crowd flow and detect any object (person) moving in the direction unlikely of the major flow. We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow. Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity to maintain a smooth crowd flow in Kaaba during the pandemic. © 2022 Tech Science Press. All rights reserved.