Learning Behavioural Context

The original publication is available at www.springerlink.co

Stel Component Analysis: Joint Segmentation, Modeling and Recognition of Objects Classes

Models that captures the common structure of an object class have appeared few years ago in the literature (Jojic and Caspi in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 212---219, 2004; Winn and Jojic in Proceedings of International Conference on Computer Vision (ICCV), pp. 756---763, 2005); they are often referred as "stel models." Their main characteristic is to segment objects in clear, often semantic, parts as a consequence of the modeling constraint which forces the regions belonging to a single segment to have a tight distribution over local measurements, such as color or texture. This self-similarity within a region in a single image is typical of many meaningful image parts, even when across different images of similar objects, the corresponding parts may not have similar local measurements. Moreover, the segmentation itself is expected to be consistent within a class, although still flexible. These models have been applied mostly to segmentation scenarios. In this paper, we extent those ideas (1) proposing to capture correlations that exist in structural elements of an image class due to global effects, (2) exploiting the segmentations to capture feature co-occurrences and (3) allowing the use of multiple, eventually sparse, observation of different nature. In this way we obtain richer models more suitable to recognition tasks. We accomplish these requirements using a novel approach we dubbed stel component analysis. Experimental results show the flexibility of the model as it can deal successfully with image/video segmentation and object recognition where, in particular, it can be used as an alternative of, or in conjunction with, bag-of-features and related classifiers, where stel inference provides a meaningful spatial partition of features

Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models

In this paper, we present an unsupervised learning framework for analyzing activities and interactions in surveillance videos. In our framework, three levels of video events are connected by Hierarchical Dirichlet Process (HDP) model: low-level visual features, simple atomic activities, and multi-agent interactions. Atomic activities are represented as distribution of low-level features, while complicated interactions are represented as distribution of atomic activities. This learning process is unsupervised. Given a training video sequence, low-level visual features are extracted based on optic flow and then clustered into different atomic activities and video clips are clustered into different interactions. The HDP model automatically decide the number of clusters, i.e. the categories of atomic activities and interactions. Based on the learned atomic activities and interactions, a training dataset is generated to train the Gaussian Process (GP) classifier. Then the trained GP models work in newly captured video to classify interactions and detect abnormal events in real time. Furthermore, the temporal dependencies between video events learned by HDP-Hidden Markov Models (HMM) are effectively integrated into GP classifier to enhance the accuracy of the classification in newly captured videos. Our framework couples the benefits of the generative model (HDP) with the discriminant model (GP). We provide detailed experiments showing that our framework enjoys favorable performance in video event classification in real-time in a crowded traffic scene

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

Weighted Bayesian Gaussian Mixture Model for Roadside LiDAR Object Detection

Background modeling is widely used for intelligent surveillance systems to detect moving targets by subtracting the static background components. Most roadside LiDAR object detection methods filter out foreground points by comparing new data points to pre-trained background references based on descriptive statistics over many frames (e.g., voxel density, number of neighbors, maximum distance). However, these solutions are inefficient under heavy traffic, and parameter values are hard to transfer from one scenario to another. In early studies, the probabilistic background modeling methods widely used for the video-based system were considered unsuitable for roadside LiDAR surveillance systems due to the sparse and unstructured point cloud data. In this paper, the raw LiDAR data were transformed into a structured representation based on the elevation and azimuth value of each LiDAR point. With this high-order tensor representation, we break the barrier to allow efficient high-dimensional multivariate analysis for roadside LiDAR background modeling. The Bayesian Nonparametric (BNP) approach integrates the intensity value and 3D measurements to exploit the measurement data using 3D and intensity info entirely. The proposed method was compared against two state-of-the-art roadside LiDAR background models, computer vision benchmark, and deep learning baselines, evaluated at point, object, and path levels under heavy traffic and challenging weather. This multimodal Weighted Bayesian Gaussian Mixture Model (GMM) can handle dynamic backgrounds with noisy measurements and substantially enhances the infrastructure-based LiDAR object detection, whereby various 3D modeling for smart city applications could be created

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

Object Tracking: Appearance Modeling And Feature Learning

Object tracking in real scenes is an important problem in computer vision due to increasing usage of tracking systems day in and day out in various applications such as surveillance, security, monitoring and robotic vision. Object tracking is the process of locating objects of interest in every frame of video frames. Many systems have been proposed to address the tracking problem where the major challenges come from handling appearance variation during tracking caused by changing scale, pose, rotation, illumination and occlusion. In this dissertation, we address these challenges by introducing several novel tracking techniques. First, we developed a multiple object tracking system that deals specially with occlusion issues. The system depends on our improved KLT tracker for accurate and robust tracking during partial occlusion. In full occlusion, we applied a Kalman filter to predict the object\u27s new location and connect the trajectory parts. Many tracking methods depend on a rectangle or an ellipse mask to segment and track objects. Typically, using a larger or smaller mask will lead to loss of tracked objects. Second, we present an object tracking system (SegTrack) that deals with partial and full occlusions by employing improved segmentation methods: mixture of Gaussians and a silhouette segmentation algorithm. For re-identification, one or more feature vectors for each tracked object are used after target reappearing. Third, we propose a novel Bayesian Hierarchical Appearance Model (BHAM) for robust object tracking. Our idea is to model the appearance of a target as combination of multiple appearance models, each covering the target appearance changes under a certain situation (e.g. view angle). In addition, we built an object tracking system by integrating BHAM with background subtraction and the KLT tracker for static camera videos. For moving camera videos, we applied BHAM to cluster negative and positive target instances. As tracking accuracy depends mainly on finding good discriminative features to estimate the target location, finally, we propose to learn good features for generic object tracking using online convolutional neural networks (OCNN). In order to learn discriminative and stable features for tracking, we propose a novel object function to train OCNN by penalizing the feature variations in consecutive frames, and the tracker is built by integrating OCNN with a color-based multi-appearance model. Our experimental results on real-world videos show that our tracking systems have superior performance when compared with several state-of-the-art trackers. In the feature, we plan to apply the Bayesian Hierarchical Appearance Model (BHAM) for multiple objects tracking