254 research outputs found
Crowd Saliency Detection via Global Similarity Structure
It is common for CCTV operators to overlook inter- esting events taking place
within the crowd due to large number of people in the crowded scene (i.e.
marathon, rally). Thus, there is a dire need to automate the detection of
salient crowd regions acquiring immediate attention for a more effective and
proactive surveillance. This paper proposes a novel framework to identify and
localize salient regions in a crowd scene, by transforming low-level features
extracted from crowd motion field into a global similarity structure. The
global similarity structure representation allows the discovery of the
intrinsic manifold of the motion dynamics, which could not be captured by the
low-level representation. Ranking is then performed on the global similarity
structure to identify a set of extrema. The proposed approach is unsupervised
so learning stage is eliminated. Experimental results on public datasets
demonstrates the effectiveness of exploiting such extrema in identifying
salient regions in various crowd scenarios that exhibit crowding, local
irregular motion, and unique motion areas such as sources and sinks.Comment: Accepted in ICPR 2014 (Oral). Mei Kuan Lim and Ven Jyn Kok share
equal contribution
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
Bayesian Generative Model Based on Color Histogram of Oriented Phase and Histogram of Oriented Optical Flow for Rare Event Detection in Crowded Scenes
In this paper, we propose a new method for rare event detection in crowded scenes using a combination of Color Histogram of Oriented Phases (CHOP) and Histogram of Oriented Optical Flow (HOOF). We propose to detect and filter spatio-temporal interest points (STIP) based on the visual saliency information of the scene. Once salient STIPs are detected, the motion and appearance information of the surrounding scene are extracted. Finally, the extracted information from normal scenes are modelled by using a Bayesian generative model (Latent Dirichlet Allocation). The rare events are detected by processing the likelihood of the current scene in regard to the obtained model. The proposed method has been tested on the publicly available UMN dataset and compared with different state-of-the-art algorithms. We have shown that our method is very competitive and provides promising results
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