Deep learning-based bird eye view social distancing monitoring using surveillance video for curbing the COVID-19 spread

A new YOLO-based method for social distancing from real-time videos

The coronavirus disease (COVID-19) is primarily disseminated through physical contact. As a precaution, it is recommended that indoor spaces have a limited number of people and at least one meter apart. This study proposes a real-time method for monitoring physical distancing compliance in indoor spaces using computer vision and deep learning techniques. The proposed method utilizes YOLO (You Only Look Once), a popular convolutional neural network-based object detection model, pre-trained on the Microsoft COCO (Common Objects in Context) dataset to detect persons and estimate their physical distance in real time. The effectiveness of the proposed method was assessed using metrics including accuracy rate, frame per second (FPS), and mean average precision (mAP). The results show that the YOLO v3 model had the most remarkable accuracy (87.07%) and mAP (89.91%). On the other hand, the highest fps rate of up to 18.71 was achieved by the YOLO v5s model. The results demonstrate the potential of the proposed method for effectively monitoring physical distancing compliance in indoor spaces, providing valuable insights for future use in other public health scenarios.

Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey

Since the start of the COVID-19 pandemic, social distancing (SD) has played an essential role in controlling and slowing down the spread of the virus in smart cities. To ensure the respect of SD in public areas, visual SD monitoring (VSDM) provides promising opportunities by (i) controlling and analyzing the physical distance between pedestrians in real-time, (ii) detecting SD violations among the crowds, and (iii) tracking and reporting individuals violating SD norms. To the authors' best knowledge, this paper proposes the first comprehensive survey of VSDM frameworks and identifies their challenges and future perspectives. Typically, we review existing contributions by presenting the background of VSDM, describing evaluation metrics, and discussing SD datasets. Then, VSDM techniques are carefully reviewed after dividing them into two main categories: hand-crafted feature-based and deep-learning-based methods. A significant focus is paid to convolutional neural networks (CNN)-based methodologies as most of the frameworks have used either one-stage, two-stage, or multi-stage CNN models. A comparative study is also conducted to identify their pros and cons. Thereafter, a critical analysis is performed to highlight the issues and impediments that hold back the expansion of VSDM systems. Finally, future directions attracting significant research and development are derived.

Face mask detection and social distance monitoring system for COVID-19 pandemic

Coronavirus triggers several respirational infections such as sneezing, coughing, and pneumonia, which transmit humans to humans through airborne droplets. According to the guidelines of the World Health Organization, the spread of COVID-19 can be mitigated by avoiding public interactions in proximity and following standard operating procedures (SOPs) including wearing a face mask and maintaining social distancing in schools, shopping malls, and crowded areas. However, enforcing the adaptation of these SOPs on a larger scale is still a challenging task. With the emergence of deep learning-based visual object detection networks, numerous methods have been proposed to perform face mask detection on public spots. However, these methods require a huge amount of data to ensure robustness in real-time applications. Also, to the best of our knowledge, there is no standard outdoor surveillance-based dataset available to ensure the efficacy of face mask detection and social distancing methods in public spots. To this end, we present a large-scale dataset comprising of 10,000 outdoor images categorized into a binary class labeling i.e., face mask, and non-face masked people to accelerate the development of automated face mask detection and social distance measurement on public spots. Alongside, we also present an end-to-end pipeline to perform real-time face mask detection and social distance measurement in an outdoor environment. Initially, existing state-of-the-art single and multi-stage object detection networks are fine-tuned on the proposed dataset to evaluate their performance in terms of accuracy and inference time. Based on better performance, YOLO-v3 architecture is further optimized by tuning its feature extraction and region proposal generation layers to improve the performance in real-time applications. Our results indicate that the presented pipeline performed better than the baseline version, showing an improvement of 5.3% in terms of accuracy.

Face mask detection in COVID-19: a strategic review

With the outbreak of the Coronavirus Disease in 2019, life seemed to be had come to a standstill. To combat the transmission of the virus, World Health Organization (WHO) announced wearing of face mask as an imperative way to limit the spread of the virus. However, manually ensuring whether people are wearing face masks or not in a public area is a cumbersome task. The exigency of monitoring people wearing face masks necessitated building an automatic system. Currently, distinct methods using machine learning and deep learning can be used effectively. In this paper, all the essential requirements for such a model have been reviewed. The need and the structural outline of the proposed model have been discussed extensively, followed by a comprehensive study of various available techniques and their respective comparative performance analysis. Further, the pros and cons of each method have been analyzed in depth. Subsequently, sources to multiple datasets are mentioned. The several software needed for the implementation are also discussed. And discussions have been organized on the various use cases, limitations, and observations for the system, and the conclusion of this paper with several directions for future research.

A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system's ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing.

Developing a real-time social distancing detection system based on YOLOv4-tiny and bird-eye view for COVID-19

COVID-19 is a virus, which is transmitted through small droplets during speech, sneezing, coughing, and mostly by inhalation between individuals in close contact. The pandemic is still ongoing and causes people to have an acute respiratory infection which has resulted in many deaths. The risks of COVID-19 spread can be eliminated by avoiding physical contact among people. This research proposes real-time AI platform for people detection, and social distancing classification of individuals based on thermal camera. YOLOv4-tiny is proposed in this research for object detection. It is a simple neural network architecture, which makes it suitable for low-cost embedded devices. The proposed model is a better option compared to other approaches for real-time detection. An algorithm is also implemented to monitor social distancing using a bird's-eye perspective. The proposed approach is applied to videos acquired through thermal cameras for people detection, social distancing classification, and at the same time measuring the skin temperature for the individuals. To tune up the proposed model for individual detection, the training stage is carried out by thermal images with various indoor and outdoor environments. The final prototype algorithm has been deployed in a low-cost Nvidia Jetson devices (Xavier and Jetson Nano) which are composed of fixed camera. The proposed approach is suitable for a surveillance system within sustainable smart cities for people detection, social distancing classification, and body temperature measurement. This will help the authorities to visualize the fulfillment of the individuals with social distancing and simultaneously monitoring their skin temperature.