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

CENTRIST3D : um descritor espaço-temporal para detecção de anomalias em vídeos de multidões

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O campo de estudo da detecção de anomalias em multidões possui uma vasta gama de aplicações, podendo-se destacar o monitoramento e vigilância de áreas de interesse, tais como aeroportos, bancos, parques, estádios e estações de trens, como uma das mais importantes. Em geral, sistemas de vigilância requerem prossionais qualicados para assistir longas gravações à procura de alguma anomalia, o que demanda alta concentração e dedicação. Essa abordagem tende a ser ineciente, pois os seres humanos estão sujeitos a falhas sob condições de fadiga e repetição devido aos seus próprios limites quanto à capacidade de observação e seu desempenho está diretamente ligado a fatores físicos e psicológicos, os quais podem impactar negativamente na qualidade de reconhecimento. Multidões tendem a se comportar de maneira complexa, possivelmente mudando de orientação e velocidade rapidamente, bem como devido à oclusão parcial ou total. Consequentemente, técnicas baseadas em rastreamento de pedestres ou que dependam de segmentação de fundo geralmente apresentam maiores taxas de erros. O conceito de anomalia é subjetivo e está sujeito a diferentes interpretações, dependendo do contexto da aplicação. Neste trabalho, duas contribuições são apresentadas. Inicialmente, avaliamos a ecácia do descritor CENsus TRansform hISTogram (CENTRIST), originalmente utilizado para categorização de cenas, no contexto de detecção de anomalias em multidões. Em seguida, propusemos o CENTRIST3D, uma versão modicada do CENTRIST que se utiliza de informações espaço-temporais para melhorar a discriminação dos eventos anômalos. Nosso método cria histogramas de características espaço-temporais de quadros de vídeos sucessivos, os quais foram divididos hierarquicamente utilizando um algoritmo modicado da correspondência em pirâmide espacial. Os resultados foram validados em três bases de dados públicas: University of California San Diego (UCSD) Anomaly Detection Dataset, Violent Flows Dataset e University of Minesota (UMN) Dataset. Comparado com outros trabalhos da literatura, CENTRIST3D obteve resultados satisfatórios nas bases Violent Flows e UMN, mas um desempenho abaixo do esperado na base UCSD, indicando que nosso método é mais adequado para cenas com mudanças abruptas em movimento e textura. Por m, mostramos que há evidências de que o CENTRIST3D é um descritor eciente de ser computado, sendo facilmente paralelizável e obtendo uma taxa de quadros por segundo suciente para ser utilizado em aplicações de tempo realAbstract: Crowd abnormality detection is a eld of study with a wide range of applications, where surveillance of interest areas, such as airports, banks, parks, stadiums and subways, is one of the most important purposes. In general, surveillance systems require well-trained personnel to watch video footages in order to search for abnormal events. Moreover, they usually are dependent on human operators, who are susceptible to failure under stressful and repetitive conditions. This tends to be an ineective approach since humans have their own natural limits of observation and their performance is tightly related to their physical and mental state, which might aect the quality of surveillance. Crowds tend to be complex, subject to subtle changes in motion and to partial or total occlusion. Consequently, approaches based on individual pedestrian tracking and background segmentation may suer in quality due to the aforementioned problems. Anomaly itself is a subjective concept, since it depends on the context of the application. Two main contributions are presented in this work. We rst evaluate the eectiveness of the CENsus TRansform hISTogram (CENTRIST) descriptor, initially designed for scene categorization, in crowd abnormality detection. Then, we propose the CENTRIST3D descriptor, a spatio-temporal variation of CENTRIST. Our method creates a histogram of spatiotemporal features from successive frames by extracting histograms of Volumetric Census Transform from a spatial representation using a modied Spatial Pyramid Matching algorithm. Additionally, we test both descriptors in three public data collections: UCSD Anomaly Detection Dataset, Violent Flows Dataset, and UMN Datasets. Compared to other works of the literature, CENTRIST3D achieved satisfactory accuracy rates on both Violent Flows and UMN Datasets, but poor performance on the UCSD Dataset, indicating that our method is more suitable to scenes with fast changes in motion and texture. Finally, we provide evidence that CENTRIST3D is an ecient descriptor to be computed, since it requires little computational time, is easily parallelizable and achieves suitable frame-per-second rates to be used in real-time applicationsMestradoCiência da ComputaçãoMestre em Ciência da Computação1406874159166/2015-2CAPESCNP

An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos

Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.Comment: 15 pages, double colum

Crowd Behavior Understanding through SIOF Feature Analysis

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over time. A non-linear SVM has then been adopted to classify dominant or large-scale crow d abnormal behaviors. The work reported has focused on: 1) online (or near real-time) detection of moving objects through a background subtraction model, namely ViBe; and to identify the saliency information as a spatial feature in addition to the optical flow of the motion foreground as the temporal feature; 2) to combine the extracted spatial and temporal features into a novel SIOF descriptor that encapsulates the global movement characteristic of a crowd; 3) the optimization of a nonlinear support vector machine (SVM) as classifier to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the BEHAVE database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements in terms of the accuracy and efficiency for detecting crowd anomalies

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

Smart video surveillance of pedestrians : fixed, aerial, and multi-camera methods

Crowd analysis from video footage is an active research topic in the field of computer vision. Crowds can be analaysed using different approaches, depending on their characteristics. Furthermore, analysis can be performed from footage obtained through different sources. Fixed CCTV cameras can be used, as well as cameras mounted on moving vehicles. To begin, a literature review is provided, where research works in the the fields of crowd analysis, as well as object and people tracking, occlusion handling, multi-view and sensor fusion, and multi-target tracking are analyses and compared, and their advantages and limitations highlighted. Following that, the three contributions of this thesis are presented: in a first study, crowds will be classified based on various cues (i.e. density, entropy), so that the best approaches to further analyse behaviour can be selected; then, some of the challenges of individual target tracking from aerial video footage will be tackled; finally, a study on the analysis of groups of people from multiple cameras is proposed. The analysis entails the movements of people and objects in the scene. The idea is to track as many people as possible within the crowd, and to be able to obtain knowledge from their movements, as a group, and to classify different types of scenes. An additional contribution of this thesis, are two novel datasets: on the one hand, a first set to test the proposed aerial video analysis methods; on the other, a second to validate the third study, that is, with groups of people recorded from multiple overlapping cameras performing different actions

Video anomaly detection with compact feature sets for online performance

Over the past decade, video anomaly detection has been explored with remarkable results. However, research on methodologies suitable for online performance is still very limited. In this paper, we present an online framework for video anomaly detection. The key aspect of our framework is a compact set of highly descriptive features, which is extracted from a novel cell structure that helps to define support regions in a coarse-to-fine fashion. Based on the scene's activity, only a limited number of support regions are processed, thus limiting the size of the feature set. Specifically, we use foreground occupancy and optical flow features. The framework uses an inference mechanism that evaluates the compact feature set via Gaussian Mixture Models, Markov Chains, and Bag-of-Words in order to detect abnormal events. Our framework also considers the joint response of the models in the local spatio-temporal neighborhood to increase detection accuracy. We test our framework on popular existing data sets and on a new data set comprising a wide variety of realistic videos captured by surveillance cameras. This particular data set includes surveillance videos depicting criminal activities, car accidents, and other dangerous situations. Evaluation results show that our framework outperforms other online methods and attains a very competitive detection performance compared with state-of-the-art non-online methods