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.Métodos para análise de ambientes de multidões são amplamente desenvolvidos na área de visão computacional. Esta tese apresenta uma abordagem para explorar características inerentes às multidões humanas - comunicação proxêmica e relações de vizinhança - para extrair características de multidões e usá-las para estimativa de fluxo de multidões e detecção e localização de anomalias. Dado o fluxo óptico produzido por qualquer método, 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 é então utilizado para filtrar o fluxo óptico original ou para extrair informações que descrevem o comportamento da multidão em diferentes resoluções, dependendo do raio do espaço pessoal selecionado na análise. Para mostrar que as características são 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 aleatória e árvores extremamente aleatórias. As duas abordagens desenvolvidas (estimativa do fluxo de multidões e detecção de anormalidades) foram testadas em conjuntos de dados públicos, envolvendo cenários de multidões humanas e comparados com métodos estado-da-arte

Generative Models for Novelty Detection Applications in abnormal event and situational changedetection from data series

Novelty detection is a process for distinguishing the observations that differ in some respect from the observations that the model is trained on. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains observations that were not known at the training time. In other words, the novelty class is often is not presented during the training phase or not well defined. In light of the above, one-class classifiers and generative methods can efficiently model such problems. However, due to the unavailability of data from the novelty class, training an end-to-end model is a challenging task itself. Therefore, detecting the Novel classes in unsupervised and semi-supervised settings is a crucial step in such tasks. In this thesis, we propose several methods to model the novelty detection problem in unsupervised and semi-supervised fashion. The proposed frameworks applied to different related applications of anomaly and outlier detection tasks. The results show the superior of our proposed methods in compare to the baselines and state-of-the-art methods

Learning Multimodal Deep Representations for Crowd Anomaly Event Detection

Anomaly event detection in crowd scenes is extremely important; however, the majority of existing studies merely use hand-crafted features to detect anomalies. In this study, a novel unsupervised deep learning framework is proposed to detect anomaly events in crowded scenes. Specifically, low-level visual features, energy features, and motion map features are simultaneously extracted based on spatiotemporal energy measurements. Three convolutional restricted Boltzmann machines are trained to model the mid-level feature representation of normal patterns. Then a multimodal fusion scheme is utilized to learn the deep representation of crowd patterns. Based on the learned deep representation, a one-class support vector machine model is used to detect anomaly events. The proposed method is evaluated using two available public datasets and compared with state-of-the-art methods. The experimental results show its competitive performance for anomaly event detection in video surveillance

Rare Events Detection and Localization In Crowded Scenes Based On Flow Signature

We introduce in this paper a novel method for rare events detection based on the optical flow signature. It aims to automatically highlight regions in videos where rare events are occurring. This kind of method can be used as an important step for many applications such as Closed-Circuit Television (CCTV) monitoring systems in order to reduce the cognitive effort of the operators by focusing their attention on the interesting regions. The proposed method exploits the properties of the Discrete Cosine Transform (DCT) applied to the magnitude and orientation maps of the optical flow. The output of the algorithm is a map where each pixel has a saliency score that indicates the presence of irregular motion regard to the scene. Based on the one class Support Vectors Machine (SVM) algorithm, a model of the frequent events is created and the rare events detection can be performed by using this model. The DCT is faster, easy to compute and gives interesting information to detect spatial irregular patterns in images [1]. Our method does not rely on any prior information of the scene and uses the saliency score as a feature descriptor. We demonstrate the potential of the proposed method on the publicly available videos dataset UCSD and show that it is competitive and outperforms some the state-of-the-art methods

Video anomaly detection using deep generative models

Video anomaly detection faces three challenges: a) no explicit definition of abnormality; b) scarce labelled data and c) dependence on hand-crafted features. This thesis introduces novel detection systems using unsupervised generative models, which can address the first two challenges. By working directly on raw pixels, they also bypass the last