Video Event Recognition for Surveillance Applications (VERSA)

VERSA provides a general-purpose framework for defining and recognizing events in live or recorded surveillance video streams. The approach for event recognition in VERSA is using a declarative logic language to define the spatial and temporal relationships that characterize a given event or activity. Doing so requires the definition of certain fundamental spatial and temporal relationships and a high-level syntax for specifying frame templates and query parameters. Although the handling of uncertainty in the current VERSA implementation is simplistic, the language and architecture is amenable to extending using Fuzzy Logic or similar approaches. VERSA's high-level architecture is designed to work in XML-based, services- oriented environments. VERSA can be thought of as subscribing to the XML annotations streamed by a lower-level video analytics service that provides basic entity detection, labeling, and tracking. One or many VERSA Event Monitors could thus analyze video streams and provide alerts when certain events are detected.Comment: Master's Thesis, University of Nebraska at Omaha, 200

Multi-object tracking in video sequences

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Análise de layout de página em jornais históricos germano-brasileiros

Orientador: Daniel WeingaertnerDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 30/08/2019Inclui referências: p. 71-75Área de concentração: Ciência da ComputaçãoResumo: Projetos de digitalizacao em massa tem surgido em todo mundo. No Brasil, um dos exemplos e a iniciativa Dokumente.br que preocupa-se em disponibilizar acervos brasileiros em lingua alema. Parte de seu acervo e composto por jornais historicos escritos com a fonte gotica Fraktur e precisam ter seus caracteres reconhecidos opticamente. Um bom desempenho nesta tarefa esta relacionado ao sucesso da etapa anterior do workflow de OCR, a analise de layout. As ferramentas OCR open source existentes nao conseguem atingir bons resultados de analise de layout neste tipo de material. Com o objetivo de corrigir esta lacuna, propomos duas abordagens para a analise de layout dos jornais da iniciativa Dokumente.br: a primeira delas, que chamamos de GBN-MHS, e uma implementacao do algoritmo "MHS 2017 System" proposto por Tran et al. (2017). A segunda abordagem e baseada em deep learning e a nomeamos de GBN-DL. Para avaliar o desempenho dos nossos metodos criamos o German-Brazilian Newspaper Dataset (GBN 1.0) e ja preparamos seu ground truth para analise de layout e tambem para OCR. Comparamos os resultados obtidos pelo analisador de layout do software Tesseract no dataset proposto e os resultados obtidos pelos metodos GBN-MHS e GBN-DL. Criamos dois cenarios de avaliacao: um composto por jornais que foram representados no conjunto de treinamento (Cenario 1) e outro com paginas de jornais que nao foram representados no conjunto de treinamento (Cenario 2). GBN-MHS e GBN-DL atingiram melhores resultados que Tesseract nos dois cenarios avaliados. No Cenario 1, GBN-DL conseguiu 92,81% de acuracia, GBN-MHS obteve 88,12% e Tesseract atingiu apenas 71,83%. No Cenario 2, GBN-DL atingiu 96,96%, GBN-MHS conseguiu 95,16% e Tesseract obteve 88,15% de acuracia. Os bons resultados atingidos pelos metodos propostos demonstram o potencial das nossas abordagens e o experimento tambem comprova como as ferramentas OCR open source existentes nao estao totalmente preparadas para trabalhar com documentos historicos. Palavras-chave: digitalizacao de jornais. sistemas OCR. analise de layout de pagina. segmentacao de paginas de jornais. OCR. OCR em Fraktur. Tesseract. OCRopy.Abstract: Mass digitization projects have emerged around the world. In Brazil, one example is the Dokumente.br initiative that aims at providing Brazilian collections in the German language. Part of its collection consists of historical newspapers written in the Gothic font Fraktur which need to have their characters recognized optically. A good performance in this task is related to the success of the previous OCR workflow step, the page layout analysis. The available open source OCR tools are not able to achieve good layout analysis results in this type of material. In order to correct this gap, two approaches to the layout analysis of the newspapers from the Dokumente.br initiative were proposed in this work: the first of these, which we call GBN-MHS, is an implementation of the "MHS 2017 System" algorithm proposed by Tran et al. (2017). The second proposal is based on deep learning and we call it GBN-DL. To evaluate the performance of the proposed methods we created the German-Brazilian Newspaper Dataset (GBN 1.0) and have already prepared its ground truth for layout analysis and also for OCR. We compared the results obtained by the layout analyzer from software Tesseract in the proposed dataset and the results obtained by the GBN-MHS and GBN-DL methods. We created two evaluation scenarios: one of them consists of newspapers that were represented in the training dataset (Scenario 1) and the other consists of newspaper pages that were not represented in the training dataset (Scenario 2). GBN-MHS and GBN-DL achieved better results than Tesseract in the two scenarios evaluated. In Scenario 1, GBN-DL achieved 92.81% in accuracy, GBN-MHS achieved 88.12% and Tesseract only 71.83%. In Scenario 2, GBN-DL reached 96.96%, GBN-MHS reached 95.16 % and Tesseract achieved 88.15 % in accuracy. The good results achieved by the proposed methods demonstrate the potential of our approaches, and the experiments also evidence that available open source OCR tools are not fully prepared to work with historical documents. Keywords: digitalization of newspapers. OCR systems. page layout analysis. page segmentation of newspapers. OCR. Fraktur OCR. Tesseract. OCRopy

Interfaz gráfica de usuario para la búsqueda de imágenes basada en imágenes: GOS- Graphic Object Searcher

El Proyecto de Final de Carrera (PFC) responde a esa necesidad de creación de herramientas de acceso a contenido multimedia, nuevas herramientas que faciliten la recuperación de toda esa información audiovisual almacenada. El Graphic Object Searcher (GOS) es una interfaz gráfica para realizar búsquedas de imágenes alojadas en grandes bases de datos a partir de una imagen ejemplo y de unos criterios de búsqueda establecidos por el usuario. La realización de PFC permite trabajar en las dos áreas tecnológicas con más auge actualmente: el sector multimedia (gestión de contenido audiovisual) y las tecnologías de la información (TIC) (informática al servicio de la comunicación). Estas dos áreas tienden a aunar esfuerzos en una sociedad abocada al uso y consumo de contenido audiovisual a través de múltiples plataformas y dispositivos en cualquier sector económico y social (ocio, formación, servicios, etc.). Cualquier profesional del sector audiovisual ha de adquirir conocimiento y experiencia en ambas áreas para cimentar su carrera

Experiments in object tracking in image sequences

This thesis explores three object tracking algorithms for image sequences. These algorithms include the ensemble tracker, the EM-like mean-shift colour-histogram tracker, and the wandering-stable-lost scale-invariant feature transform (WSL-SIFT) tracker. The algorithms are radically different from one another. Despite their differences, they are evaluated on the same publicly available, moderately sized, research data sets which include 129 test cases in 13 different scenes. The results aid in fostering an understanding of their respective behaviours and in highlighting their flaws and failures. Lastly, an implementation setup is described that is suited to large-scale, grid computing, batch testing of these algorithms. Results clearly indicate that none of the evaluated trackers are suited to general purpose use. However, one may intelligently choose a tracker for a well-defined application by analysing the known scene characteristics