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One of the main and most effective measures to contain the recent viral outbreak is the maintenance of the so-called Social Distancing (SD). To comply with this constraint, governments are adopting restrictions over the minimum inter-personal distance between people. Given this actual scenario, it is crucial to massively measure the compliance to s...
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... is strictly linked to the definition of people gatherings, namely groups, and as such, it depends on its spatial organization and the number of people involved. In general, the surrounding space around a person is characterized by interpersonal distance classes [38], namely: intimate, personal, peri-personal or social, and public spaces (see Fig. 2), all associated to different social distances, in turn, also dependent by the degree of kinship and familiarity between the subjects and by the geometrical configuration and size of the environment in which an interplay occurs. A blind application of SD rules, encouraging to stay further than 1-2 meters, will eliminate an entire ...
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Citations
... Access considerations (Abdul Nasir et al., 2021), (Cristani et al., 2020), (M. Hameed Al-Delfi and S. Salman, 2022), (Mustafa and Ahmed, 2022). ...
... _ The analysis of distance, spatial organization, users count, and surrounding area using predetermined measures (Cristani et al., 2020). ...
... Social distancing is defined as a kinetic-spatial mechanism that keeps people at a safe distance from one another and prevents people from gathering in cramped and crowded spaces. Social distancing limits close contact between people to reduce the spread of epidemics (Cristani et al., 2020). This main category includes three other sub-indicators and (27) Preventive solutions related to addressing the spatial and motor aspects that can contribute to promoting and evaluating the effectiveness of social distancing, as listed in Table 5. ...
The recent outbreak of epidemics created health challenges and emerging requirements that revealed weaknesses and increased alerts about the contribution of the existing buildings' design to increasing the possibility of epidemics' spread. Those risks necessitated focusing on designing more effectively resilient buildings to epidemics. The study focuses on improving the safety aspects of existing building design from an architectural design perspective. Therefore, this study proposes a theoretical framework for a set of preventive indicators to evaluate the existing building design as a resilient architectural system in its response to epidemics. These indicators have been identified and selected based on an extensive examination of the literature and the most effective practices for preventing and controlling epidemics. Those preventive indicators covered all the various aspects of the design of the existing building, including the effectiveness of social distancing, indoor air quality, indoor environmental quality, control engineering and preventive. Using these indicators, architecture professionals and policymakers can evaluate the effectiveness of existing buildings in reducing the spread of epidemics and making the necessary improvements to create a more resilient environment. The proposed preventive indicators aim to contribute to developing epidemic-resilient architecture and promote the creation of healthier and safer living environments for occupants.
... [6] In this project they used wireless technology with distancing monitoring through microchips which is one of its advantages but it is quite complex in manufacturing the product which is the disadvantage of this project which was published in the year 2020 in the ieee journal. [7] The Visual Social Distance Problem, in this project they were using proxemics with person detection and pose estimation which is the main advantage of it but it requires an additional process to give the message intimation which was published in the year 2020 in the month of june. [8] The title named COVID SAFE in this project they used an automated IoT based monitoring system which is the main advantage of this system but its disadvantage is it consumes more time to give the results which was published in the ieee journal in October 2020. ...
Due to the COVID-19 pandemic there have been many life losses and health issues especially respiratory syndrome. To overcome this it is mandatory that people should wear mask and strictly follow social distancing in public places. In order to bring it as a strict rule it would be easy if we monitor the social distance in a frequent basis. This project as named social distance monitor will help all individuals to maintain the required social distance. It will continuously calculate distance between the individuals using this device and others approaching them and if distance is violated and not maintain properly will intimate and make them to correct it. Thereby social distancing can be maintained perfectly in all public places particularly focusing on institutions like schools, colleges, hospitals, workplaces etc. In case if distance has been violated continuously information can be sent to authorities via e-mail, SMS.
... However, a phenomenon is ''far from eyes, far from the heart." Social distance does not mean people have to weaken their social bonds [51]. Furthermore, in Sweden, physical distancing was implemented like in other countries to curb the spread of the virus but differently, as the people are directed towards the urban nature where they can maintain their positive psychology and maintain urban resilience in the long and short terms [17]. ...
Amid the Covid-19 pandemic, lifestyles changed completely. This new normality damages human psychology and mental health. Hence, new approaches must be considered while shaping public spaces to accommodate the pandemic life. This paper aims to show the importance of exploiting outdoor spaces to save people’s mental health. Accordingly, an online survey is conducted and analyzed by Statistical Package for the Social Sciences (SPSS) for more precise answers. Afterward, the most important public spaces during the pandemic are extracted; consequently, another questionnaire has been held to validate these items. The last one has been run through a machine learning technique to classify and categorize the users’ different preferences in three situations only. It was found that 85,17% of the sample declared the importance of outdoor public spaces. However, future research is needed to rethink urban spaces’ design and to relocate the activities done within indoor public spaces to the outdoors to maintain human mental health.
... Since the first appearance of COVID-19, the IoT community has promptly reacted, through ideas that became demonstrators in unprecedented short times. From social distance analysis systems [1] and the analysis of the correct use of Personal Protective Equipment (PPE), air monitoring control, systems aim at assisting elderly and impaired people [2], to disinfection systems, specific research directions have been started or boosted, leading to theoretical and technological advances and promoting further research in some cases. ...
... A star topology is employed, where all the devices are connected to an orchestrator which acts as the central node and controller of the network. Some devices (smart lights and cameras) have a direct connection to the BOX-IO controller, while for others (automatic doors based on [4], air quality sensors, air sanitizer, and controlled mechanical ventilation), 1 The project also involved modules of psychological support, which here we prefer to omit for the sake of space. an intermediate gateway is required to provide them a WiFi interface. ...
... Crowd detection for anti-covid purposes differs from classic crowd surveillance [1] in that it becomes crucial to capture the distance at which people are from each other, trying to avoid the formation of gatherings of people standing at a distance of less than 2m. However, all this represents a minor challenge at a technological level, given that calibration systems for the mapping of moving objects on the plane are rich and the problem can be said to be solved. ...
... But their model is not suitable for places with more traffic as they had lesser number of training samples. M. Cristani et al. (Cristani et al., 2020) proposed a Visual Social Distancing Problem to truly detect potentially dangerous situations by calculating the inter-personal (Sakhapara et al., 2018) for the purpose of detecting faces in group images and identifying the people present. This approach uses advanced algorithms to recognize and distinguish individuals in a group photo. ...
The new coronavirus that produced the pandemic known as COVID-19 has been going across the world for a while. Nearly every area of development has been impacted by COVID-19. There is an urgent need for improvement in the healthcare system. However, this contagious illness can be controlled by appropriately donning a facial mask. If people keep a strong social distance and wear face masks, COVID-19 can be controlled. A method for detecting these violations is proposed in this paper. These infractions include failing to wear a facemask and failing to maintain social distancing. To train a deep learning architecture, a dataset compiled from several sources is used. To compute the distance between two people in a particular area and also predicts the people wearing and not wearing the mask, The proposed system makes use of YOLOv3 architecture and computer vision. The goal of this research is to provide valuable tool for reducing the transmission of this contagious disease in various environments, including streets and supermarkets. The proposed system is evaluated using the COCO dataset. It is evident from the experimental analysis that the proposed system performs well in predicting the people wearing the mask because it has acquired an accuracy of 99.2% and an F1-score of 0.99.
... These approaches require special hardware made for the specific applications. A recent work [35] proposes a non-intrusive warning system with softer omnidirectional Another work [12] defines social distancing monitoring as a visual social distancing problem. This work introduces a skeleton for a detection-based approach to inter-personal distance measuring. ...
... Since it would have no information as to how much of the body is captured inside the bounding box. This is the main advantage of our method over other more trivial implementations [12,27]. ...
The corona virus pandemic has introduced limitations which were previously not a cause for concern. Chief among them are wearing face masks in public and constraints on the physical distance between people as an effective measure to reduce the virus spread. Visual surveillance systems, which are common in urban environments and initially commissioned for security surveillance, can be re-purposed to help limit the spread of COVID-19 and prevent future pandemics. In this work, we propose a novel integration technique for real-time pose estimation and multiple human tracking in a pedestrian setting, primarily for social distancing, using CCTV camera footage. Our technique promises a sizeable increase in processing speed and improved detection in very low-resolution scenarios. Using existing surveillance systems, pedestrian pose estimation, tracking and localization for social distancing (PETL4SD) is proposed for measuring social distancing, which combines the output of multiple neural networks aided with fundamental 2D/3D vision techniques. We leverage state-of-the-art object and pose estimation algorithms, combining their strengths, for increase in speed and improvement in detections. These detections are then tracked using a bespoke version of the FASTMOT algorithm. Temporal and analogous estimation techniques are used to deal with occlusions when estimating posture. Projective geometry along with the aforementioned posture tracking is then used to localize the pedestrians. Inter-personal distances are calculated and locally inspected to detect possible violations of the social distancing rules. Furthermore, a “smart violations detector” is employed which estimates if people are together based on their current actions and eliminates false social distancing violations within groups. Finally, distances are intuitively visualized with the right perspective. All implementation is in real time and is performed on Python. Experimental results are provided to validate our proposed method quantitatively and qualitatively on public domain datasets using only a single CCTV camera feed as input. Our results show our technique to outperform the baseline in speed and accuracy in low-resolution scenarios. The code of this work will be made publicly available on GitHub at https://github.com/bilalze/PETL4SD.
... As alternative, solutions based on computer vision [16]- [20] to determine the visual social distance [21] are potentially less intrusive. They do not require explicit commitment by the people to carry mobile/wearable devices and can be adopted using standard cameras deployed for video surveillance purposes. ...
The recent pandemic emergency raised many challenges regarding the countermeasures aimed at containing the virus spread, and constraining the minimum distance between people resulted in one of the most effective strategies. Thus, the implementation of autonomous systems capable of monitoring the so-called
social distance
gained much interest. In this paper, we aim to address this task leveraging a single RGB frame without additional depth sensors. In contrast to existing single-image alternatives failing when ground localization is not available, we rely on single image depth estimation to perceive the 3D structure of the observed scene and estimate the distance between people. During the setup phase, a straightforward calibration procedure, leveraging a scale-aware SLAM algorithm available even on consumer smartphones, allows us to address the scale ambiguity affecting single image depth estimation. We validate our approach through indoor and outdoor images employing a calibrated LiDAR + RGB camera asset. Experimental results highlight that our proposal enables sufficiently reliable estimation of the inter-personal distance to monitor social distancing effectively. This fact confirms that despite its intrinsic ambiguity, if appropriately driven single image depth estimation can be a viable alternative to other depth perception techniques, more expensive and not always feasible in practical applications. Our evaluation also highlights that our framework can run reasonably fast and comparably to competitors, even on pure CPU systems. Moreover, its practical deployment on low-power systems is around the corner.
... The paper demonstrates the use of deep learning approaches for face mask recognition and face classification using clustering for the dataset. [6] this paper develops a more novel face detection system of principal component analysis (PCA) and convolution neural network (CNN) combined with a probabilistic Bayes classifier, SVM (support vector machine), and MLP (multilayer perceptron algorithm) to detect multi-face model for mask detection. [7]this paper describes the human detection model which measures the distance between humans using a sound wave sensor. ...
Globally, numerous preventive measures were taken to treat the COVID-19 epidemic. Face masks and social distancing were two of the most crucial practices for limiting the spread of novel viruses. With YOLOv5 and a pre-trained framework, we present a novel method of complex mask detection. The primary objective is to detect complex different face masks at higher rates and obtain accuracy of about 94% to 99% on real-time video feeds. The proposed methodology also aims to implement a structure to detect social distance based on a YOLOv5 architecture for controlling, monitoring, accomplishing, and reducing the interaction of physical communication among people in the day-to-day environment. In order for the framework to be trained for the different crowd datasets from the top, it was trained for the human contrasts. Based on the pixel information and the violation threshold, the Euclidean distance between peoples is determined as soon as the people in the video are spotted. In the results, this social distance architecture is described as providing effective monitoring and alerting.
... One way to approach this problem is visual social distancing (VSD), where the interpersonal distances are automatically measured from the images or videos. A comprehensive overview of the VSD problem, including the main challenges and connections to social studies, is provided in (Cristani et al., 2020). ...
The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitable benchmark for developing such algorithms. Collecting images with measured ground-truth pair-wise distances using different camera settings is cumbersome. Moreover, performance evaluation for these algorithms is not straightforward, and there is no widely accepted evaluation protocol. In this paper, we provide an image dataset with measured pair-wise social distances under different camera positions and settings. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate such algorithms. We also propose an automatic social distance estimation method that can be applied on general photo collections. Our method is a hybrid method that combines deep learning-based object detection and human pose estimation with projective geometry. The method can be applied on uncalibrated single images with known focal length and sensor size. The results on our benchmark are encouraging with 91% human detection rate and only 38.24% average relative distance estimation error among the detected people.
... The recent advancements of computer vision technology and widespread deployment of fixed and mobile cameras (e.g., on drones) attracted a number of works analyzing social distancing on the basis of video analysis [28]. From a technological point of view this is probably one of the best solution, but it is hindered by strong privacy concerns about accessing multiple video feeds. ...
Wireless networks are commonly used in public spaces, universities and public institutions and provide accurate and easily accessible information to monitor the mobility and behavior of users. Following the application of containment measures during the recent pandemic, we analyse extensive data from the WiFi network in a University Campus in Italy during three periods, corresponding to partial lockdown, partial opening, and almost complete opening. We measure the probability distributions of groups and link activation at Wi-Fi Access Points, investigating how different areas are used in the presence of restrictions. We rank the hotspots and the area they cover according to their crowding and to the probability of link formation, which is the relevant variable in determining potential outbreaks. We consider a recently proposed epidemic model on simplicial temporal networks and we use the measured distributions to infer the change in the reproduction number in the three phases. Our data show that additional measures are necessary to limit the epidemic spreading in the total opening phase, due to the dramatic increase in the number of contacts.
