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Intelligent video surveillance systems : an algorithmic approach
This book will provide an overview of techniques for visual monitoring including video surveillance and human activity understanding. It will present the basic techniques of processing video from static cameras, starting with object detection and tracking. The author will introduce further video analytic modules including face detection, trajectory analysis and object classification. Examining system design and specific problems in visual surveillance, such as the use of multiple cameras and moving cameras, the author will elaborate on privacy issues focusing on approaches where automatic processing can help protect privacy-- Provided by publisher.
Book
Traffic flow estimation with data from a video surveillance camera
This study addresses the problem of traffic flow estimation based on the data from a video surveillance camera. Target problem here is formulated as counting and classifying vehicles by their driving direction. This subject area is in early development, and the focus of this work is only one of the busiest crossroads in city Chelyabinsk, Russia. To solve the posed problem, we employed the state-of-the-art Faster R-CNN two-stage detector together with SORT tracker. A simple regions-based heuristic algorithm was used to classify vehicles movement direction. The baseline performance of the Faster R-CNN was enhanced by several modifications: focal loss, adaptive feature pooling, additional mask branch, and anchors optimization. To train and evaluate detector, we gathered 982 video frames with more than 60,000 objects presented in various conditions. The experimental results show that the proposed system can count vehicles and classify their driving direction during weekday rush hours with mean absolute percentage error that is less than 10%. The dataset presented here might be further used by other researches as a challenging test or additional training data.
Journal Article
Disease surveillance : technological contributions to global health security
Providing an overview of disease surveillance, this text frames a roadmap of how newer technologies may allow all countries of the world to reach compliance with the IHR (International Health Regulations) established by the World Health Organization as it pertains to disease detection.
Book
Timeliness and completeness of weekly surveillance data reporting on epidemic prone diseases in Uganda, 2020–2021
Introduction
Disease surveillance provides vital data for disease prevention and control programs. Incomplete and untimely data are common challenges in planning, monitoring, and evaluation of health sector performance, and health service delivery. Weekly surveillance data are sent from health facilities using mobile tracking (mTRAC) program, and synchronized into the District Health Information Software version 2 (DHIS2). The data are then merged into district, regional, and national level datasets. We described the completeness and timeliness of weekly surveillance data reporting on epidemic prone diseases in Uganda, 2020–2021.
Methods
We abstracted data on completeness and timeliness of weekly reporting of epidemic-prone diseases from 146 districts of Uganda from the DHIS2.Timeliness is the proportion of all expected weekly reports that were submitted to DHIS2 by 12:00pm Monday of the following week. Completeness is the proportion of all expected weekly reports that were completely filled and submitted to DHIS2 by 12:00pm Wednesday of the following week. We determined the proportions and trends of completeness and timeliness of reporting at national level by year, health region, district, health facility level, and facility ownership.
Results
National average reporting timeliness and completeness was 44% and 70% in 2020, and 49% and 75% in 2021. Eight of the 15 health regions achieved the target for completeness of ≥ 80%; Lango attained the highest (93%) in 2020, and Karamoja attained 96% in 2021. None of the regions achieved the timeliness target of ≥ 80% in either 2020 or 2021. Kampala District had the lowest completeness (38% and 32% in 2020 and 2021, respectively) and the lowest timeliness (19% in both 2020 and 2021). Referral hospitals and private owned health facilities did not attain any of the targets, and had the poorest reporting rates throughout 2020 and 2021.
Conclusion
Weekly surveillance reporting on epidemic prone diseases improved modestly over time, but timeliness of reporting was poor. Further investigations to identify barriers to reporting timeliness for surveillance data are needed to address the variations in reporting.
Journal Article
Passwords : philology, security, authentication
Today we regard cryptology, the technical science of ciphers and codes, and philology, the humanistic study of human languages, as separate domains of activity. But the contiguity of these two domains is a historical fact with an institutional history. From the earliest documented techniques for the statistical analysis of text to the computational philology of early twenty-first-century digital humanities, what Brian Lennon calls \"crypto-philology\" has flourished alongside, and sometimes directly served, imperial nationalism and war. Lennon argues that while computing's humanistic applications are as historically important as its mathematical and technical origins, they are no less marked by the priorities of institutions devoted to signals intelligence. The convergence of philology with cryptology, Lennon suggests, is embodied in the password, an artifact of the linguistic history of computing that each of us uses every day to secure access to personal data and other resources. The password is a site where philology and cryptology, and their contiguous histories, meet in everyday life, as the natural-language dictionary becomes an instrument of the hacker's exploit.-- Provided by publisher
Book
Determining Gaps in Publicly Shared SARS-CoV-2 Genomic Surveillance Data by Analysis of Global Submissions
Viral genomic surveillance has been a critical source of information during the COVID-19 pandemic, but publicly available data can be sparse, concentrated in wealthy countries, and often made public weeks or months after collection. We used publicly available viral genomic surveillance data submitted to GISAID and GenBank to examine sequencing coverage and lag time to submission during 2020-2021. We compared publicly submitted sequences by country with reported infection rates and population and also examined data based on country-level World Bank income status and World Health Organization region. We found that as global capacity for viral genomic surveillance increased, international disparities in sequencing capacity and timeliness persisted along economic lines. Our analysis suggests that increasing viral genomic surveillance coverage worldwide and decreasing turnaround times could improve timely availability of sequencing data to inform public health action.
Journal Article
Digital rights and privacy
\"At this point, it is almost impossible to avoid having a digital footprint. Social media, streaming websites, navigation applications, online shopping websites, and search engines generate a large amount of data about users' digital habits. Tech companies have used this data to 'optimize' their products and allow them to better predict users' behaviors, but the collection and use of data has raised new questions about the right to digital privacy. The rise of the internet and social media has also caused concerns about the type of content that should be made available and whether tech companies or the government have a responsibility and legal right to control it.\" -- Publisher description.
Book
Views on increased federal access to state and local National Syndromic Surveillance Program data: a nominal group technique study with state and local epidemiologists
Background
US public health authorities use syndromic surveillance to monitor and detect public health threats, conditions, and trends in near real-time. Nearly all US jurisdictions that conduct syndromic surveillance send their data to the National Syndromic Surveillance Program (NSSP), operated by the US. Centers for Disease Control and Prevention. However, current data sharing agreements limit federal access to state and local NSSP data to only multi-state regional aggregations. This limitation was a significant challenge for the national response to COVID-19. This study seeks to understand state and local epidemiologists’ views on increased federal access to state NSSP data and identify policy opportunities for public health data modernization.
Methods
In September 2021, we used a virtual, modified nominal group technique with twenty regionally diverse epidemiologists in leadership positions and three individuals representing national public health organizations. Participants individually generated ideas on benefits, concerns, and policy opportunities relating to increased federal access to state and local NSSP data. In small groups, participants clarified and grouped the ideas into broader themes with the assistance of the research team. An web-based survey was used to evaluate and rank the themes using five-point Likert importance questions, top-3 ranking questions, and open-ended response questions.
Results
Participants identified five benefit themes for increased federal access to jurisdictional NSSP data, with the most important being improved cross-jurisdiction collaboration (mean Likert = 4.53) and surveillance practice (4.07). Participants identified nine concern themes, with the most important concerns being federal actors using jurisdictional data without notice (4.60) and misinterpretation of data (4.53). Participants identified eleven policy opportunities, with the most important being involving state and local partners in analysis (4.93) and developing communication protocols (4.53).
Conclusion
These findings identify barriers and opportunities to federal-state-local collaboration critical to current data modernization efforts. Syndromic surveillance considerations warrant data-sharing caution. However, identified policy opportunities share congruence with existing legal agreements, suggesting that syndromic partners are closer to agreement than they might realize. Moreover, several policy opportunities (i.e., including state and local partners in data analysis and developing communication protocols) received consensus support and provide a promising path forward.
Journal Article
Your face belongs to us : a secretive startup's quest to end privacy as we know it
\"In this riveting feat of reporting, Kashmir Hill illuminates the improbable rise of Clearview AI and how Hoan Ton-That, a computer engineer and Richard Schwartz, a Giuliani associate, launched a terrifying facial recognition app with society-altering potential. They were assisted by a cast of controversial characters, including conservative provocateur Charles Johnson and billionaire Trump backer Peter Thiel. The app can scan a blurry portrait, and, in just seconds, collect every instance of a person's online life. It can find your name, your social media profiles, your friends and family, even your home address (as well as photos of you that you may not even have known existed). The story of Clearview AI opens up a window into a larger, more urgent one about our tortured relationship to technology, the way it entertains and seduces us even as it steals our privacy and lays us bare to bad actors in politics, criminal justice, and tech. This technology has been quietly growing more powerful for decades. Ubiquitous in China and Russia, it was also developed by American companies, including Google and Facebook, who decided it was too radical to release. That did not stop Clearview. They gave demos of the tech to interested private investors and contracted it out to hundreds of law enforcement agencies around the country. American law enforcement, including the Department of Homeland Security, has already used it to arrest people for everything from petty theft to assault. Without regulation it could expand the reach of policing-as it has in China and Russia-to a terrifying, dystopian level\"-- Provided by publisher.
Book
Participatory, Virologic, and Wastewater Surveillance Data to Assess Underestimation of COVID-19 Incidence, Germany, 2020–2024
Using participatory, virologic, and wastewater surveillance systems, we estimated when and to what extent reported data of adult COVID-19 cases underestimated COVID-19 incidence in Germany. We also examined how case underestimation evolved over time. Our findings highlight how community-based surveillance systems can complement official notification systems for respiratory disease dynamics.
Journal Article