Verification of Smoke Detection in Video Sequences Based on Spatio-temporal Local Binary Patterns

AbstractThe early smoke detection in outdoor scenes using video sequences is one of the crucial tasks of modern surveillance systems. Real scenes may include objects that are similar to smoke with dynamic behavior due to low resolution cameras, blurring, or weather conditions. Therefore, verification of smoke detection is a necessary stage in such systems. Verification confirms the true smoke regions, when the regions similar to smoke are already detected in a video sequence. The contributions are two-fold. First, many types of Local Binary Patterns (LBPs) in 2D and 3D variants were investigated during experiments according to changing properties of smoke during fire gain. Second, map of brightness differences, edge map, and Laplacian map were studied in Spatio-Temporal LBP (STLBP) specification. The descriptors are based on histograms, and a classification into three classes such as dense smoke, transparent smoke, and non-smoke was implemented using Kullback-Leibler divergence. The recognition results achieved 96–99% and 86–94% of accuracy for dense smoke in dependence of various types of LPBs and shooting artifacts including noise

АЛГОРИТМ АНАЛИЗА ДИНАМИЧЕСКИХ ТЕКСТУР

Recognizing dynamic patterns based on visual processing is significant for many applications such as remote monitoring for the prevention of natural disasters, e.g. forest fires, various types of surveillance, e.g. traffic monitoring, background subtraction in challenging environments, e.g. outdoor scenes with vegetation, homeland security applications and scientific studies of animal behavior. In the context of surveillance, recognizing dynamic patterns is of significance to isolate activities of interest (e.g. fire) from distracting background (e.g. windblown vegetation and changes in scene illumination).Methods: pattern recognition, computer vision.Results: This paper presents video based image processing algorithm with samples usually containing a cluttered background. According to the spatiotemporal features, four categorized groups were formulated. Dynamic texture recognition algorithm refers image objects to one of this group. Motion, color, facial, energy Laws and ELBP features are extracted for dynamic texture categorization. Classification based on boosted random forest.Practical relevance: Experimental results show that the proposed method is feasible and effective for video-based dynamic texture categorization. Averaged classification accuracy on the all video images is 95.2%.Постановка проблемы: Обнаружение динамических текстур на видеоизображениях в настоящее время находит все более широкое применение в системах компьютерного зрения. Например, обнаружение дыма и пламени в системах экологического мониторинга, анализ автомобильного трафика при мониторинге загруженности дорог, и в других системах. Поиск объекта интереса на динамическом фоне часто бывает затруднен за счет похожих текстурных признаков или признаков движения у фона и искомого объекта. В связи с этим возникает необходимость разработки алгоритма классификации динамических текстур для выделения объектов интереса на динамическом фоне.Методы: распознавание образов, компьютерное зрение.Результаты: В данной работе рассматривается обработка видеоизображений содержащих объекты с динамическим поведением на динамическом фоне, такие как вода, туман, пламя, текстиль на ветру и др. Разработан алгоритм отнесения объектов видеоизображения к одной из четырех предлагаемых категорий. Извлекаются признаки движения, цветовые особенности, фрактальности, энергетические признаки Ласа, строятся ELBP-гистограммы. В качестве классификатора использован бустинговый случайный лес.Практическая значимость: Разработан метод, позволяющий разделить динамические текстур на категории: по типу движения (периодическое и хаотичное) и типу объектов интереса (природные и искусственные). Экспериментальные исследования подтверждают эффективность предложенного алгоритма для отнесения объектов изображения к той или иной категории. Средняя точность классификации составила 95.2%

Video Based Flame and Smoke Detection

В работе предложен алгоритм обнаружения пожара по видеоданным на открытых пространствах, когда традиционными способами на основе датчиков химического состава воздуха или температуры обнаружение дыма и пламени невозможно. Обнаружение дыма и пламени выполняется параллельно, пожар считается найденным в случае детектирования одного объекта: дыма или пламени. Алгоритм нахождения дыма и пламени основан на анализе пространственно-временных признаков. На первом этапе обнаружения дыма выполняется поиск движения с использованием алгоритма сопоставления блоков, затем производится хроматический анализ движущихся областей, учет турбулентности. Классификация областей-кандидатов производится с использованием машины опорных векторов. Верификация выполнена на базе пространственно-временных локальных бинарных шаблонов. Для обнаружения пламени взята функция Background Subtraction библиотеки компьютерного зрения OpenCV, выполнен учет цветовых особенностей пламени и анализ его динамических свойств. Для проведения экспериментальных исследований использованы базы данных видеопоследовательностей Билькентского университета и Dyntex. Дополнительно репрезентативность тестового набора видеороликов повышена данными с реальных камер видеонаблюдения, в том числе полученными в ночное время. Количество кадров тестовых видеопоследовательностей составило 44 406, общая продолжительность роликов – 40 мин. Средняя точность обнаружения дыма составила 98 %, пламени – 94,9 %. Ложноположительные срабатывания при обнаружении дыма и пламени в среднем равны 3,46 %. Таким образом, экспериментальные исследования подтверждают эффективность предложенного алгоритма обнаружения пламени и дыма по видеопоследовательностям на открытых пространствахThe early fire detection in outdoor scenes using video sequences is one of crucial tasks of modern surveillance systems in urban and terrestrial natural environment. A conventional way of video analysis is to design a good background model and to track a motion selectively. Color, contours, fractal properties, and transparency, are considered the main spatial properties of smoke and flame in a still image or a single frame. Smoke detections algorithm steps. First, local smoke regions are detected based on motion estimation and chromatic analysis. The clustering of such local regions provides global smoke regions in a scene. At this stage, smoke and non-smoke regions are analyzed in order to exclude errors of false rejection. The suspicious region is extracted by using blockmatching algorithm. Second, global regions are verified by using statistical and temporal features. In this research, smoke colored blocks and turbulence characteristics. Verification based on spatiotemporal local binary patterns. An automatic flame detection method uses the features of fire, such as the moving parameters, chromatic components, and geometrical (flickering) features. A candidate fire region is determined according to the color component ratio and motion cue of fire flame obtained by background subtraction. The flame color probability is then estimated based threshold value in the combination of RGB and YSV color spaces. The motion probability obtained is by employing the background model with Background Subtractor function in OpenCV (Open Source Computer Vision Library). Flames flicker in height, size and in brightness. Video based flame detection algorithms often analyze flickering of pixel intensities over time to detect flames. In this study we investigate five different pixel intensity flickering features based on methods presented in previous work. For flickering features we calculate geometry, compare frequency of initial frame with fire re-gion candidate, and check the change in the size of the rectangular flame candidate block.Flame and smoke regions classifier using support vector machine. Video based flame and smoke detection is carried out in parallel.For experimental researches the database of dynamic textures Dyntex and database of Bilkent University were used. The developed method of smoke detection on video provides 94.9–98% of accuracy for fire detection. Experimental results show that the proposed method is feasible and effective for video based flame and smoke detectio

Trends in the global market for the transfer of intellectual property

The article is aimed at conducting a comprehensive study of opportunities and prospects of development of methodology and practice of organization of innovation processes in the economies of various countries, development of recommendations to increase the efficiency of innovation activity.The relevance of this topic is due to the need to create effective mechanisms of expert and analytical support of a high level of innovation development, the importance of providing measures to support technologies - «catalysts» socio-economic development of economic agents, the key role of technology transfer as a condition for ensuring and maintaining innovative activity of economic agents. Keywords: innovation, innovative technologies, intellectual property transfer

BUILDING THE PROFILE OF THE SUBSCRIBER OF MOBILE NETWORKS BASED ON ONTOLOGICAL APPROACH

Цель. В связи с увеличением числа абонентов мобильных сетей, используемых абонентами устройств, а также высокой активностью абонентов агрегируемая об атрибутах абонентов информация необходима для выстраивания рекомендательных функций систем телекоммуникационных компаний, проведения маркетинговых инициатив, повышения качества оказываемых услуг, при прогнозировании потребностей и желаний клиентов, а также для многих других функций. Статья посвящена рассмотрение методов, направленных на формализацию предметной области при построении профилей абонентов мобильной связи.Методы. В работе рассматриваются метод формальных понятий, а также модель представления информации на концептуальном уровне в контексте представления знаний об абонентах мобильной связи.Результаты. На основе изучения методов структурирования знаний о предметной области авторами предлагается модель представления качественной и количественной информации об объекте исследования с использованием онтологического подхода.Purpose. In connection with the increase in the number of mobile network subscribers used by device users, as well as the high activity of subscribers, information aggregated about the attributes of subscribers is necessary for building advisory functions of telecommunications companies’ systems, conducting marketing initiatives, improving the quality of services provided, predicting the needs and desires of customers, and for many other functions. The article is devoted to the consideration of methods aimed at formalization of the subject domain in the construction of profiles of mobile communication subscribers.Methods. The paper considers the method of formal concepts, as well as the model of information representation at the conceptual level in the context of knowledge representation about mobile communication subscribers.Results. On the basis of studying the methods of structuring knowledge of the subject domain, the authors propose a model for presenting qualitative and quantitative information about the object of research using the ontological approach

Dynamic texture recognition under adverse lighting and weather conditions for outdoor environments

Recognizing dynamic patterns based on visual processing is significant for many applications. In this paper dynamic texture recognition focuses on outdoor scenarios where a crisis event might occur (i.e. fire in a forest, floods/flooding etc.) Real outdoor scenes may include the objects with dynamic behaviour due to illumination, blurring, or weather conditions effects. Under bad weather conditions the imaging systems is degraded and produce low visibility images. In this work precipitation artefacts and lightning effects for dynamic texture analysis were studied. Experimental results show that the proposed method of weather and adverse lighting effects compensation is feasible and effective for videobased dynamic texture analysis under bad weather conditions

Artificial neural network technology for lips reading

The paper presents the use of neural networks for the task of automated speech reading by lips articulation. Speech recognition is performed in two stages. First, a face search is performed and the lips area is selected in a separate frame of the video sequence using Haar features. Then the sequence of frames goes to the input of deep learning convolutional and recurrent neural networks for speech viseme recognition. Experimental studies were carried out using independently obtained videos with Russian-speaking speakers

Verification of Smoke Detection in Video Sequences Based on Spatio-temporal Local Binary Patterns

Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала.The early smoke detection in outdoor scenes using video sequences is one of the crucial tasks of modern surveillance systems. Real scenes may include objects that are similar to smoke with dynamic behavior due to low resolution cameras, blurring, or weather conditions. Therefore, verification of smoke detection is a necessary stage in such systems. Verification confirms the true smoke regions, when the regions similar to smoke are already detected in a video sequence. The contributions are two-fold. First, many types of Local Binary Patterns (LBPs) in 2D and 3D variants were investigated during experiments according to changing properties of smoke during fire gain. Second, map of brightness differences, edge map, and Laplacian map were studied in Spatio-Temporal LBP (STLBP) specification. The descriptors are based on histograms, and a classification into three classes such as dense smoke, transparent smoke, and non-smoke was implemented using Kullback-Leibler divergence. The recognition results achieved 96–99% and 86–94% of accuracy for dense smoke in dependence of various types of LPBs and shooting artifacts including noise

Texture analysis in watermarking paradigms

Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала.Digital watermarking algorithms have been developed rapidly as a response on the challenges caused by various internet attacks that are distorted the content of the host image and watermark partially or fully. In this paper, the issues of texture analysis with a goal to detect the most suitable image areas for embedding are discussed. The statistical and model-based methods are investigated as a trade-off between the computational cost and quality of the detected areas, where the embedded bits of a watermark could be the most invisible for a human vision. The criteria for detection of such areas based on the textural, contrast, illumination, and color coherence of the host image and watermark are formulated. The experiments show that the statistical methods based on the gradient oriented Local Binary Patterns (LBP) provide better computational time regarding to fractal estimation of textural image areas

Texture analysis in watermarking paradigms

Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала.Digital watermarking algorithms have been developed rapidly as a response on the challenges caused by various internet attacks that are distorted the content of the host image and watermark partially or fully. In this paper, the issues of texture analysis with a goal to detect the most suitable image areas for embedding are discussed. The statistical and model-based methods are investigated as a trade-off between the computational cost and quality of the detected areas, where the embedded bits of a watermark could be the most invisible for a human vision. The criteria for detection of such areas based on the textural, contrast, illumination, and color coherence of the host image and watermark are formulated. The experiments show that the statistical methods based on the gradient oriented Local Binary Patterns (LBP) provide better computational time regarding to fractal estimation of textural image areas