Internal Control Automatic Physical Distance Detection System for Patient Care against a Possible COVID-19 Infection within a Health Center

Currently, COVID-19 is still present in our environment, affecting various people regardless of age. From its appearance in the city of Wuhan (China), it caused a crisis in the health system due to its rapid transmission of contagion and the lack of medicines to combat this virus, therefore, it was declared a pandemic, due to the high mortality rate that occurred around the world. To combat this viral disease, various biosecurity measures were established such as the mandatory use of a face mask and physical distancing at least 1 meter, with physical distancing being the biosecurity measure that is not respected, worse in closed places such as medical centers where a focus of contagion could originate for various patients. By respecting physical distancing, it would avoid causing a crowd that would cause disorder in the health center, therefore, it is important to maintain distancing to avoid the spread of COVID-19. In view of this problem, in this article an automatic physical distance detection system was carried out for patient care in the face of a possible COVID-19 infection within a health center, enforcing this important biosecurity measure and preventing the spread of COVID-19. Through the development of the proposed system, 98.79% efficiency was obtained in calculating the physical distancing of patients who are inside the health center, taking only 40 seconds to analyze the images of the patients

Anomaly Detection in Traffic Surveillance Videos Using Deep Learning

In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abnormal events or incidents regarding traffic on different roads and highways, such as traffic jams, traffic congestion, and vehicle accidents. Mostly in accidents, people are helpless and some die due to the unavailability of emergency treatment on long highways and those places that are far from cities. This research proposes a methodology for detecting accidents automatically through surveillance videos. A review of the literature suggests that convolutional neural networks (CNNs), which are a specialized deep learning approach pioneered to work with grid-like data, are effective in image and video analysis. This research uses CNNs to find anomalies (accidents) from videos captured by the VTSS and implement a rolling prediction algorithm to achieve high accuracy. In the training of the CNN model, a vehicle accident image dataset (VAID), composed of images with anomalies, was constructed and used. For testing the proposed methodology, the trained CNN model was checked on multiple videos, and the results were collected and analyzed. The results of this research show the successful detection of traffic accident events with an accuracy of 82% in the traffic surveillance system videos.publishedVersio

Security-Camera Proposal for the Dynamy Youth Center

Dynamy is an educational program that sponsors a Youth Center for high schools students. Recently, Dynamy officials have considered security cameras to secure the building from outsiders. To assess Dynamy\u27s Security Camera needs, I went to the University of Pennsylvania to learn from the most secure educational facility in America. I also met with professional CCTV installers from ADT Security, who even gave a free on-site estimate. I was able to draft a security camera proposal for the Dynamy Youth Center. The proposal asks for 8 cameras to be installed by Dynamy officials to secure the facility\u27s computer labs, conference rooms, office areas, and entrance ways. The security camera proposal explains how to buy a CCTV system, where to place cameras, and how to route cabling

Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem

Retail shoplifting is one of the most prevalent forms of theft and has accounted for over one billion GBP in losses for UK retailers in 2018. An automated approach to detecting behaviours associated with shoplifting using surveillance footage could help reduce these losses. Until recently, most state-of-the-art vision-based approaches to this problem have relied heavily on the use of black box deep learning models. While these models have been shown to achieve very high accuracy, this lack of understanding on how decisions are made raises concerns about potential bias in the models. This limits the ability of retailers to implement these solutions, as several high-profile legal cases have recently ruled that evidence taken from these black box methods is inadmissible in court. There is an urgent need to develop models which can achieve high accuracy while providing the necessary transparency. One way to alleviate this problem is through the use of social signal processing to add a layer of understanding in the development of transparent models for this task. To this end, we present a social signal processing model for the problem of shoplifting prediction which has been trained and validated using a novel dataset of manually annotated shoplifting videos. The resulting model provides a high degree of understanding and achieves accuracy comparable with current state of the art black box methods