4,576 research outputs found
Learning Behavioural Context
The original publication is available at www.springerlink.co
Understanding Vehicular Traffic Behavior from Video: A Survey of Unsupervised Approaches
Recent emerging trends for automatic behavior analysis and understanding from infrastructure video are reviewed. Research has shifted from high-resolution estimation of vehicle state and instead, pushed machine learning approaches to extract meaningful patterns in aggregates in an unsupervised fashion. These patterns represent priors on observable motion, which can be utilized to describe a scene, answer behavior questions such as where is a vehicle going, how many vehicles are performing the same action, and to detect an abnormal event. The review focuses on two main methods for scene description, trajectory clustering and topic modeling. Example applications that utilize the behavioral modeling techniques are also presented. In addition, the most popular public datasets for behavioral analysis are presented. Discussion and comment on future directions in the field are also provide
Análise de multidões usando coerência de vizinhança local
Large numbers of crowd analysis methods using computer vision have been developed in the past years. This dissertation presents an approach to explore characteristics inherent to human crowds – proxemics, and neighborhood relationship – with the purpose of extracting crowd features and using them for crowd flow estimation and anomaly detection and localization. Given the optical flow produced by any method, the proposed approach compares the similarity of each flow vector and its neighborhood using the Mahalanobis distance, which can be obtained in an efficient manner using integral images. This similarity value is then used either to filter the original optical flow or to extract features that describe the crowd behavior in different resolutions, depending on the radius of the personal space selected in the analysis. To show that the extracted features are indeed relevant, we tested several classifiers in the context of abnormality detection. More precisely, we used Recurrent Neural Networks, Dense Neural Networks, Support Vector Machines, Random Forest and Extremely Random Trees. The two developed approaches (crowd flow estimation and abnormality detection) were tested on publicly available datasets involving human crowded scenarios and compared with state-of-the-art methods.MĂ©todos para análise de ambientes de multidões sĂŁo amplamente desenvolvidos na área de visĂŁo computacional. Esta tese apresenta uma abordagem para explorar caracterĂsticas inerentes Ă s multidões humanas - comunicação proxĂŞmica e relações de vizinhança - para extrair caracterĂsticas de multidões e usá-las para estimativa de fluxo de multidões e detecção e localização de anomalias. Dado o fluxo Ăłptico produzido por qualquer mĂ©todo, a abordagem proposta compara a similaridade de cada vetor de fluxo e sua vizinhança usando a distância de Mahalanobis, que pode ser obtida de maneira eficiente usando imagens integrais. Esse valor de similaridade Ă© entĂŁo utilizado para filtrar o fluxo Ăłptico original ou para extrair informações que descrevem o comportamento da multidĂŁo em diferentes resoluções, dependendo do raio do espaço pessoal selecionado na análise. Para mostrar que as caracterĂsticas sĂŁo realmente relevantes, testamos vários classificadores no contexto da detecção de anormalidades. Mais precisamente, usamos redes neurais recorrentes, redes neurais densas, máquinas de vetores de suporte, floresta aleatĂłria e árvores extremamente aleatĂłrias. As duas abordagens desenvolvidas (estimativa do fluxo de multidões e detecção de anormalidades) foram testadas em conjuntos de dados pĂşblicos, envolvendo cenários de multidões humanas e comparados com mĂ©todos estado-da-arte
Aerospace medicine and biology: A continuing bibliography with indexes, supplement 204
This bibliography lists 140 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980
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