IoT as Non-Phaarmaceutical Interventions for the Safety of Living Environments in COVID-19 Pandemic Age

The outbreak of the Sars-Cov-2 pandemic has changed our perception of safety in shared and public living environments including healthcare facilities, shops, schools, and enterprises. The Internet of Things (IoT) represents a suitable solution for managing anti-pandemic smart devices (e.g., UV lights, smart cameras, etc.) and increasing citizens’ safety in public health crises. In this paper, we highlighted how IoT technologies can be exploited as non-pharmaceutical interventions presenting the SAFE PLACE project as an implementation of this concept. The project meant to design and develop an IoT system to ensure the safety and salubrity of shared environments. Advanced algorithms will be exploited to detect and classify humans’ presence, gathering, usage of personal protective equipment, and considering carefully the privacy protection of individuals.

Future Movement Future – REJECTED

In a not-too-distant future, an anonymous researcher and their team applied for funding to develop their newest invention: a new algorithmic model for smart cameras that would allow people to analyze the movement of cars at a previously unheard-of scale. This system was said to enable new forms of predictive capabilities to emerge: the algorithm would be able to, for example, predict the route drivers wanted to take but had not yet taken—including, for example, their occult inner desires for getting away with a secret lover. A panel of academic reviewers from three different universities audited and reviewed the proposed system. All that is left are segments of the video-report resulting from this meeting, which became an urban legend among technology researchers. The short film “Future Movement Future – REJECTED” is the story of a dystopian surveillance future that was barred by institutional refusal. It importantly reminds us about how total surveillance, the “almighty algorithmic eye,” may end up seeing-predicting much less than imagining-dreaming.

A Manycore Vision Processor for Real-Time Smart Cameras

Real-time image processing and computer vision systems are now in the mainstream of technologies enabling applications for cyber-physical systems, Internet of Things, augmented reality, and Industry 4.0. These applications bring the need for Smart Cameras for local real-time processing of images and videos. However, the massive amount of data to be processed within short deadlines cannot be handled by most commercial cameras. In this work, we show the design and implementation of a manycore vision processor architecture to be used in Smart Cameras. With massive parallelism exploration and application-specific characteristics, our architecture is composed of distributed processing elements and memories connected through a Network-on-Chip. The architecture was implemented as an FPGA overlay, focusing on optimized hardware utilization. The parameterized architecture was characterized by its hardware occupation, maximum operating frequency, and processing frame rate. Different configurations ranging from one to eighty-one processing elements were implemented and compared to several works from the literature. Using a System-on-Chip composed of an FPGA integrated into a general-purpose processor, we showcase the flexibility and efficiency of the hardware/software architecture. The results show that the proposed architecture successfully allies programmability and performance, being a suitable alternative for future Smart Cameras.

Problems and possibilities of using video analytics in the fields of education and entertainment

Быстрые изменения и совершенствование технологий произвели революцию в современном мире. Взаимодействие человека и компьютера (HCI) развивалось в течение определенного периода, трансформируя многие аспекты нашей жизни, включая то, как мы учимся. В настоящее время студенты могут извлечь выгоду из быстрого обмена информацией, доступности в Интернете и практической реализации того, что ранее преподавалось только в книгах. Опыт обучения и компетентность зависят от того, насколько хорошо предмет преподается студентам и через какую среду. Книги и текстовые ресурсы со временем зарекомендовали себя как отличный способ доставки и использовались на протяжении веков. Аудио-и видеоматериалы также оказались эффективным способом доставки информации, поскольку они обеспечивают хорошее количество богатого контента за относительно короткий период, что привело к повышению мотивации учащихся в классе и изменению восприятия преподавателей. Однако отсутствие погружения и контроля делает обучение на основе видео менее личным, чем интерактивные классы и моделирование реальной жизни. Видеонаблюдение с помощью видеоаналитики может быть развернуто для мониторинга территорий в определенное время суток. Например, как только школа открывается, не должно быть много активности на парковке или в определенных местах вокруг школы. В таких ситуациях интеллектуальные камеры с видеоаналитикой могут использоваться для обнаружения активности в тех областях, которые представляют интерес, чтобы предупредить службу безопасности школы о том, что что-то может потребовать их внимания. Радиолокационное обнаружение идеально подходит для периметров, где устройство может быть ненавязчиво настроено для оповещения, когда кто-то входит в определенную зону. Rapid changes and improvements in technology have revolutionized the modern world. Human-computer Interaction (HCI) has evolved over a period of time, transforming many aspects of our lives, how we learn. Currently, students can benefit from the rapid exchange of information, accessibility on the Internet, and practical implementation of what was previously taught only in books. Learning experience and competence depend on how well the subject is taught to students and through what medium. Books and text resources have proven to be a great delivery method over time and have been used for centuries. Audio and video materials have also proven to be an effective way to deliver information, as they provide a good amount of rich content in a relatively short period, which has led to increased motivation of students in the classroom and a change in the perception of teachers. However, the lack of immersion and control makes video-based learning less personal than interactive classes and real-life simulations. Video surveillance using video analytics can be deployed to monitor territories at certain times of the day. For example, once a school opens, there shouldn't be a lot of activity in the parking lot or in certain places around the school. In such situations, smart cameras with video analytics can be used to detect activity in areas of interest, to warn the school security service that something may require their attention. Radar detection is ideal for perimeters, where the device can be unobtrusively configured to alert when someone enters a certain area.

Multiprocessor system for detecting and tracking marine vessels based on the "system on chip" technology

Транспортные интеллектуальные видеосистемы наблюдения, включающие в свой состав умные камеры с функциями анализа видеоинформации для обеспечения безопасности транспорта, в том числе и морских объектов, представляют собой наиболее востребованные системы, синтезируемые на базе субмикронных технологий. При этом важнейшая особенность таких систем заключается в автоматизированной обработке и анализе видеоинформации, полученной от различных камер наблюдения. Определена базовая концепция обнаружения и слежения за объектами на основе применения метода формирования опорных векторов. Применение технологии «система на кристалле» позволяет оперативно обнаружить подозрительное поведение объектов, на основе встроенных интеллектуальных сложно-функциональных блоков, входящих в состав предложенной транспортной интеллектуальной видеосистемы. В данном исследовании предлагается конкретная архитектура системы видеонаблюдения на базе программируемой многопроцессорной системы с аппаратным ускорителем основных вычислений. В состав видеосистемы наблюдения входит схема аппаратного ускорителя формирования опорных векторов, составленная классификационной частью с использованием внутренней памяти. Экспериментальный раздел показывает возможность реализации предлагаемой системы с точки зрения производительности и потребляемых ресурсов на базе программируемых схем. Рассматривается реализация системы видеонаблюдения с использованием гибридной архитектуры на базе мультипроцессора и формирования опорных векторов на базе аппаратного ускорителя. Transport intelligent video surveillance systems, which include smart cameras with video information analysis functions to ensure the safety of transport, including marine objects, are the most popular systems synthesized on the basis of submicron technologies. At the same time, the most important feature of such systems is the automated processing and analysis of video information obtained from various surveillance cameras. Basic concept of object detection and tracking is defined based on application of method of reference vectors formation. The use of the "system on chip" technology allows you to quickly detect suspicious behavior of objects, based on the built-in intelligent complex-functional blocks that are part of the proposed transport intelligent video system. In this paper, we propose a specific architecture of a video surveillance system based on a programmable multiprocessor system with a hardware accelerator for basic computing. The video surveillance system includes a circuit of a hardware accelerator for generating reference vectors, compiled by a classification part using internal memory. The experimental section shows the possibility of implementing the proposed system in terms of performance and resource consumption based on programmable circuits FPGA. Implementation of video surveillance system using hybrid architecture based on multiprocessor and formation of reference vectors based on hardware accelerator is considered.

Artificial vision system for object classification in real time using Raspberry Pi and a Web camera

Computer vision systems are an essential part in industrial automation tasks such as: identification, selection, measurement, defect detection and quality control in parts and components. There are smart cameras used to perform tasks, however, their high acquisition and maintenance cost is restrictive. In this work, a novel low-cost artificial vision system is proposed for classifying objects in real time, using the Raspberry Pi 3B + embedded system, a Web camera and the Open CV artificial vision library. The suggested technique comprises the training of a supervised classification system of the Haar Cascade type, with image banks of the object to be recognized, subsequently generating a predictive model which is put to the test with real-time detection, as well as the calculation for the prediction error. This seeks to build a powerful vision system, affordable and also developed using free software.