QUIS-CAMPI: Biometric Recognition in Surveillance Scenarios

The concerns about individuals security have justified the increasing number of surveillance cameras deployed both in private and public spaces. However, contrary to popular belief, these devices are in most cases used solely for recording, instead of feeding intelligent analysis processes capable of extracting information about the observed individuals. Thus, even though video surveillance has already proved to be essential for solving multiple crimes, obtaining relevant details about the subjects that took part in a crime depends on the manual inspection of recordings. As such, the current goal of the research community is the development of automated surveillance systems capable of monitoring and identifying subjects in surveillance scenarios. Accordingly, the main goal of this thesis is to improve the performance of biometric recognition algorithms in data acquired from surveillance scenarios. In particular, we aim at designing a visual surveillance system capable of acquiring biometric data at a distance (e.g., face, iris or gait) without requiring human intervention in the process, as well as devising biometric recognition methods robust to the degradation factors resulting from the unconstrained acquisition process. Regarding the first goal, the analysis of the data acquired by typical surveillance systems shows that large acquisition distances significantly decrease the resolution of biometric samples, and thus their discriminability is not sufficient for recognition purposes. In the literature, diverse works point out Pan Tilt Zoom (PTZ) cameras as the most practical way for acquiring high-resolution imagery at a distance, particularly when using a master-slave configuration. In the master-slave configuration, the video acquired by a typical surveillance camera is analyzed for obtaining regions of interest (e.g., car, person) and these regions are subsequently imaged at high-resolution by the PTZ camera. Several methods have already shown that this configuration can be used for acquiring biometric data at a distance. Nevertheless, these methods failed at providing effective solutions to the typical challenges of this strategy, restraining its use in surveillance scenarios. Accordingly, this thesis proposes two methods to support the development of a biometric data acquisition system based on the cooperation of a PTZ camera with a typical surveillance camera. The first proposal is a camera calibration method capable of accurately mapping the coordinates of the master camera to the pan/tilt angles of the PTZ camera. The second proposal is a camera scheduling method for determining - in real-time - the sequence of acquisitions that maximizes the number of different targets obtained, while minimizing the cumulative transition time. In order to achieve the first goal of this thesis, both methods were combined with state-of-the-art approaches of the human monitoring field to develop a fully automated surveillance capable of acquiring biometric data at a distance and without human cooperation, designated as QUIS-CAMPI system. The QUIS-CAMPI system is the basis for pursuing the second goal of this thesis. The analysis of the performance of the state-of-the-art biometric recognition approaches shows that these approaches attain almost ideal recognition rates in unconstrained data. However, this performance is incongruous with the recognition rates observed in surveillance scenarios. Taking into account the drawbacks of current biometric datasets, this thesis introduces a novel dataset comprising biometric samples (face images and gait videos) acquired by the QUIS-CAMPI system at a distance ranging from 5 to 40 meters and without human intervention in the acquisition process. This set allows to objectively assess the performance of state-of-the-art biometric recognition methods in data that truly encompass the covariates of surveillance scenarios. As such, this set was exploited for promoting the first international challenge on biometric recognition in the wild. This thesis describes the evaluation protocols adopted, along with the results obtained by the nine methods specially designed for this competition. In addition, the data acquired by the QUIS-CAMPI system were crucial for accomplishing the second goal of this thesis, i.e., the development of methods robust to the covariates of surveillance scenarios. The first proposal regards a method for detecting corrupted features in biometric signatures inferred by a redundancy analysis algorithm. The second proposal is a caricature-based face recognition approach capable of enhancing the recognition performance by automatically generating a caricature from a 2D photo. The experimental evaluation of these methods shows that both approaches contribute to improve the recognition performance in unconstrained data.A crescente preocupação com a segurança dos indivíduos tem justificado o crescimento do número de câmaras de vídeo-vigilância instaladas tanto em espaços privados como públicos. Contudo, ao contrário do que normalmente se pensa, estes dispositivos são, na maior parte dos casos, usados apenas para gravação, não estando ligados a nenhum tipo de software inteligente capaz de inferir em tempo real informações sobre os indivíduos observados. Assim, apesar de a vídeo-vigilância ter provado ser essencial na resolução de diversos crimes, o seu uso está ainda confinado à disponibilização de vídeos que têm que ser manualmente inspecionados para extrair informações relevantes dos sujeitos envolvidos no crime. Como tal, atualmente, o principal desafio da comunidade científica é o desenvolvimento de sistemas automatizados capazes de monitorizar e identificar indivíduos em ambientes de vídeo-vigilância. Esta tese tem como principal objetivo estender a aplicabilidade dos sistemas de reconhecimento biométrico aos ambientes de vídeo-vigilância. De forma mais especifica, pretende-se 1) conceber um sistema de vídeo-vigilância que consiga adquirir dados biométricos a longas distâncias (e.g., imagens da cara, íris, ou vídeos do tipo de passo) sem requerer a cooperação dos indivíduos no processo; e 2) desenvolver métodos de reconhecimento biométrico robustos aos fatores de degradação inerentes aos dados adquiridos por este tipo de sistemas. No que diz respeito ao primeiro objetivo, a análise aos dados adquiridos pelos sistemas típicos de vídeo-vigilância mostra que, devido à distância de captura, os traços biométricos amostrados não são suficientemente discriminativos para garantir taxas de reconhecimento aceitáveis. Na literatura, vários trabalhos advogam o uso de câmaras Pan Tilt Zoom (PTZ) para adquirir imagens de alta resolução à distância, principalmente o uso destes dispositivos no modo masterslave. Na configuração master-slave um módulo de análise inteligente seleciona zonas de interesse (e.g. carros, pessoas) a partir do vídeo adquirido por uma câmara de vídeo-vigilância e a câmara PTZ é orientada para adquirir em alta resolução as regiões de interesse. Diversos métodos já mostraram que esta configuração pode ser usada para adquirir dados biométricos à distância, ainda assim estes não foram capazes de solucionar alguns problemas relacionados com esta estratégia, impedindo assim o seu uso em ambientes de vídeo-vigilância. Deste modo, esta tese propõe dois métodos para permitir a aquisição de dados biométricos em ambientes de vídeo-vigilância usando uma câmara PTZ assistida por uma câmara típica de vídeo-vigilância. O primeiro é um método de calibração capaz de mapear de forma exata as coordenadas da câmara master para o ângulo da câmara PTZ (slave) sem o auxílio de outros dispositivos óticos. O segundo método determina a ordem pela qual um conjunto de sujeitos vai ser observado pela câmara PTZ. O método proposto consegue determinar em tempo-real a sequência de observações que maximiza o número de diferentes sujeitos observados e simultaneamente minimiza o tempo total de transição entre sujeitos. De modo a atingir o primeiro objetivo desta tese, os dois métodos propostos foram combinados com os avanços alcançados na área da monitorização de humanos para assim desenvolver o primeiro sistema de vídeo-vigilância completamente automatizado e capaz de adquirir dados biométricos a longas distâncias sem requerer a cooperação dos indivíduos no processo, designado por sistema QUIS-CAMPI. O sistema QUIS-CAMPI representa o ponto de partida para iniciar a investigação relacionada com o segundo objetivo desta tese. A análise do desempenho dos métodos de reconhecimento biométrico do estado-da-arte mostra que estes conseguem obter taxas de reconhecimento quase perfeitas em dados adquiridos sem restrições (e.g., taxas de reconhecimento maiores do que 99% no conjunto de dados LFW). Contudo, este desempenho não é corroborado pelos resultados observados em ambientes de vídeo-vigilância, o que sugere que os conjuntos de dados atuais não contêm verdadeiramente os fatores de degradação típicos dos ambientes de vídeo-vigilância. Tendo em conta as vulnerabilidades dos conjuntos de dados biométricos atuais, esta tese introduz um novo conjunto de dados biométricos (imagens da face e vídeos do tipo de passo) adquiridos pelo sistema QUIS-CAMPI a uma distância máxima de 40m e sem a cooperação dos sujeitos no processo de aquisição. Este conjunto permite avaliar de forma objetiva o desempenho dos métodos do estado-da-arte no reconhecimento de indivíduos em imagens/vídeos capturados num ambiente real de vídeo-vigilância. Como tal, este conjunto foi utilizado para promover a primeira competição de reconhecimento biométrico em ambientes não controlados. Esta tese descreve os protocolos de avaliação usados, assim como os resultados obtidos por 9 métodos especialmente desenhados para esta competição. Para além disso, os dados adquiridos pelo sistema QUIS-CAMPI foram essenciais para o desenvolvimento de dois métodos para aumentar a robustez aos fatores de degradação observados em ambientes de vídeo-vigilância. O primeiro é um método para detetar características corruptas em assinaturas biométricas através da análise da redundância entre subconjuntos de características. O segundo é um método de reconhecimento facial baseado em caricaturas automaticamente geradas a partir de uma única foto do sujeito. As experiências realizadas mostram que ambos os métodos conseguem reduzir as taxas de erro em dados adquiridos de forma não controlada

Online Mutual Foreground Segmentation for Multispectral Stereo Videos

The segmentation of video sequences into foreground and background regions is a low-level process commonly used in video content analysis and smart surveillance applications. Using a multispectral camera setup can improve this process by providing more diverse data to help identify objects despite adverse imaging conditions. The registration of several data sources is however not trivial if the appearance of objects produced by each sensor differs substantially. This problem is further complicated when parallax effects cannot be ignored when using close-range stereo pairs. In this work, we present a new method to simultaneously tackle multispectral segmentation and stereo registration. Using an iterative procedure, we estimate the labeling result for one problem using the provisional result of the other. Our approach is based on the alternating minimization of two energy functions that are linked through the use of dynamic priors. We rely on the integration of shape and appearance cues to find proper multispectral correspondences, and to properly segment objects in low contrast regions. We also formulate our model as a frame processing pipeline using higher order terms to improve the temporal coherence of our results. Our method is evaluated under different configurations on multiple multispectral datasets, and our implementation is available online.Comment: Preprint accepted for publication in IJCV (December 2018

Improved robustness and efficiency for automatic visual site monitoring

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 219-228).Knowing who people are, where they are, what they are doing, and how they interact with other people and things is valuable from commercial, security, and space utilization perspectives. Video sensors backed by computer vision algorithms are a natural way to gather this data. Unfortunately, key technical issues persist in extracting features and models that are simultaneously efficient to compute and robust to issues such as adverse lighting conditions, distracting background motions, appearance changes over time, and occlusions. In this thesis, we present a set of techniques and model enhancements to better handle these problems, focusing on contributions in four areas. First, we improve background subtraction so it can better handle temporally irregular dynamic textures. This allows us to achieve a 5.5% drop in false positive rate on the Wallflower waving trees video. Secondly, we adapt the Dalal and Triggs Histogram of Oriented Gradients pedestrian detector to work on large-scale scenes with dense crowds and harsh lighting conditions: challenges which prevent us from easily using a background subtraction solution. These scenes contain hundreds of simultaneously visible people. To make using the algorithm computationally feasible, we have produced a novel implementation that runs on commodity graphics hardware and is up to 76 faster than our CPU-only implementation. We demonstrate the utility of this detector by modeling scene-level activities with a Hierarchical Dirichlet Process.(cont.) Third, we show how one can improve the quality of pedestrian silhouettes for recognizing individual people. We combine general appearance information from a large population of pedestrians with semi-periodic shape information from individual silhouette sequences. Finally, we show how one can combine a variety of detection and tracking techniques to robustly handle a variety of event detection scenarios such as theft and left-luggage detection. We present the only complete set of results on a standardized collection of very challenging videos.by Gerald Edwin Dalley.Ph.D

Carried baggage detection and recognition in video surveillance with foreground segmentation

Security cameras installed in public spaces or in private organizations continuously record video data with the aim of detecting and preventing crime. For that reason, video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis, have gained high interest in recent years. In this thesis, the primary focus is on two key aspects of video analysis, reliable moving object segmentation and carried object detection & identification. A novel moving object segmentation scheme by background subtraction is presented in this thesis. The scheme relies on background modelling which is based on multi-directional gradient and phase congruency. As a post processing step, the detected foreground contours are refined by classifying the edge segments as either belonging to the foreground or background. Further contour completion technique by anisotropic diffusion is first introduced in this area. The proposed method targets cast shadow removal, gradual illumination change invariance, and closed contour extraction. A state of the art carried object detection method is employed as a benchmark algorithm. This method includes silhouette analysis by comparing human temporal templates with unencumbered human models. The implementation aspects of the algorithm are improved by automatically estimating the viewing direction of the pedestrian and are extended by a carried luggage identification module. As the temporal template is a frequency template and the information that it provides is not sufficient, a colour temporal template is introduced. The standard steps followed by the state of the art algorithm are approached from a different extended (by colour information) perspective, resulting in more accurate carried object segmentation. The experiments conducted in this research show that the proposed closed foreground segmentation technique attains all the aforementioned goals. The incremental improvements applied to the state of the art carried object detection algorithm revealed the full potential of the scheme. The experiments demonstrate the ability of the proposed carried object detection algorithm to supersede the state of the art method

Video Registration for Multimodal Surveillance Systems

RÉSUMÉ Au cours de la dernière décennie, la conception et le déploiement de systèmes de surveillance par caméras thermiques et visibles pour l'analyse des activités humaines a retenu l'attention de la communauté de la vision par ordinateur. Les applications de l'imagerie thermique-visible pour l'analyse des activités humaines couvrent différents domaines, notamment la médecine, la sécurité à bord d'un véhicule et la sécurité des personnes. La motivation derrière un tel système est l'amélioration de la qualité des données dans le but ultime d'améliorer la performance du système de surveillance. Une difficulté fondamentale associée à un système d'imagerie thermique-visible est la mise en registre précise de caractéristiques et d'informations correspondantes à partir d'images avec des différences significatives dans les propriétés des signaux. Dans un cas, on capte des informations de couleur (lumière réfléchie) et dans l'autre cas, on capte la signature thermique (énergie émise). Ce problème est appelé mise en registre d'images et de séquences vidéo. La vidéosurveillance est l'un des domaines d'application le plus étendu de l'imagerie multi-spectrale. La vidéosurveillance automatique dans un environnement réel, que ce soit à l'intérieur ou à l'extérieur, est difficile en raison d'un nombre élevé de facteurs environnementaux tels que les variations d'éclairage, le vent, le brouillard, et les ombres. L'utilisation conjointe de différentes modalités permet d'augmenter la fiabilité des données d'entrée, et de révéler certaines informations sur la scène qui ne sont pas perceptibles par un système d'imagerie unimodal. Les premiers systèmes multimodaux de vidéosurveillance ont été conçus principalement pour des applications militaires. Mais de nos jours, en raison de la réduction du prix des caméras thermiques, ce sujet de recherche s'étend à des applications civiles ayant une variété d'objectifs. Les approches pour la mise en registre d'images pour un système multimodal de vidéosurveillance automatique sont divisées en deux catégories fondées sur la dimension de la scène: les approches qui sont appropriées pour des grandes scènes où les objets sont lointains, et les approches qui conviennent à de petites scènes où les objets sont près des caméras. Dans la littérature, ce sujet de recherche n'est pas bien documenté, en particulier pour le cas de petites scènes avec objets proches. Notre recherche est axée sur la conception de nouvelles solutions de mise en registre pour les deux catégories de scènes dans lesquels il y a plusieurs humains. Les solutions proposées sont incluses dans les quatre articles qui composent cette thèse. Nos méthodes de mise en registre sont des prétraitements pour d'autres tâches d'analyse vidéo telles que le suivi, la localisation de l'humain, l'analyse de comportements, et la catégorisation d'objets. Pour les scènes avec des objets lointains, nous proposons un système itératif qui fait de façon simultanée la mise en registre thermique-visible, la fusion des données et le suivi des personnes. Notre méthode de mise en registre est basée sur une mise en correspondance de trajectoires (en utilisant RANSAC) à partir desquelles on estime une matrice de transformation affine pour transformer globalement des objets d'avant-plan d'une image sur l'autre image. Notre système proposé de vidéosurveillance multimodale est basé sur un nouveau mécanisme de rétroaction entre la mise en registre et le module de suivi, ce qui augmente les performances des deux modules de manière itérative au fil du temps. Nos méthodes sont conçues pour des applications en ligne et aucune calibration des caméras ou de configurations particulières ne sont requises. Pour les petites scènes avec des objets proches, nous introduisons le descripteur Local Self-Similarity (LSS), comme une mesure de similarité viable pour mettre en correspondance les régions du corps humain dans des images thermiques et visibles. Nous avons également démontré théoriquement et quantitativement que LSS, comme mesure de similarité thermique-visible, est plus robuste aux différences entre les textures des régions correspondantes que l'information mutuelle (IM), qui est la mesure de similarité classique pour les applications multimodales. D'autres descripteurs viables, y compris Histogram Of Gradient (HOG), Scale Invariant Feature Transform (SIFT), et Binary Robust Independent Elementary Feature (BRIEF) sont également surclassés par LSS. En outre, nous proposons une approche de mise en registre utilisant LSS et un mécanisme de votes pour obtenir une carte de disparité stéréo dense pour chaque région d'avant-plan dans l'image. La carte de disparité qui en résulte peut alors être utilisée pour aligner l'image de référence sur la seconde image. Nous démontrons que notre méthode surpasse les méthodes dans l'état de l'art, notamment les méthodes basées sur l'information mutuelle. Nos expériences ont été réalisées en utilisant des scénarios réalistes de surveillance d'humains dans une scène de petite taille. En raison des lacunes des approches locales de correspondance stéréo pour l'estimation de disparités précises dans des régions de discontinuité de profondeur, nous proposons une méthode de correspondance stéréo basée sur une approche d'optimisation globale. Nous introduisons un modèle stéréo approprié pour la mise en registre d'images thermique-visible en utilisant une méthode de minimisation de l'énergie en conjonction avec la méthode Belief Propagation (BP) comme méthode pour optimiser l'affectation des disparités par une fonction d'énergie. Dans cette méthode, nous avons intégré les informations de couleur et de mouvement comme contraintes douces pour améliorer la précision d'affectation des disparités dans les cas de discontinuités de profondeur. Bien que les approches de correspondance globale soient plus gourmandes au niveau des ressources de calculs par rapport aux approches de correspondance locale basée sur la stratégie Winner Take All (WTA), l'algorithme efficace BP et la programmation parallèle (OpenMP) en C++ que nous avons utilisés dans notre implémentation, permettent d'accélérer le temps de traitement de manière significative et de rendre nos méthodes viables pour les applications de vidéosurveillance. Nos méthodes sont programmées en C++ et utilisent la bibliothèque OpenCV. Nos méthodes sont conçues pour être facilement intégrées comme prétraitement pour toute application d'analyse vidéo. En d'autres termes, les données d'entrée de nos méthodes pourraient être un flux vidéo en ligne, et pour une analyse plus approfondie, un nouveau module pourrait être ajouté en aval à notre schéma algorithmique. Cette analyse plus approfondie pourrait être le suivi d'objets, la localisation d'êtres humains, et l'analyse de trajectoires pour les applications de surveillance multimodales de grandes scène. Aussi, Il pourrait être l'analyse de comportements, la catégorisation d'objets, et le suivi pour les applications sur des scènes de tailles réduites.---------ABSTRACT Recently, the design and deployment of thermal-visible surveillance systems for human analysis attracted a lot of attention in the computer vision community. Thermal-visible imagery applications for human analysis span different domains including medical, in-vehicle safety system, and surveillance. The motivation of applying such a system is improving the quality of data with the ultimate goal of improving the performance of targeted surveillance system. A fundamental issue associated with a thermal-visible imaging system is the accurate registration of corresponding features and information from images with high differences in imaging characteristics, where one reflects the color information (reflected energy) and another one reflects thermal signature (emitted energy). This problem is named Image/video registration. Video surveillance is one of the most extensive application domains of multispectral imaging. Automatic video surveillance in a realistic environment, either indoor or outdoor, is difficult due to the unlimited number of environmental factors such as illumination variations, wind, fog, and shadows. In a multimodal surveillance system, the joint use of different modalities increases the reliability of input data and reveals some information of the scene that might be missed using a unimodal imaging system. The early multimodal video surveillance systems were designed mainly for military applications. But nowadays, because of the reduction in the price of thermal cameras, this subject of research is extending to civilian applications and has attracted more interests for a variety of the human monitoring objectives. Image registration approaches for an automatic multimodal video surveillance system are divided into two general approaches based on the range of captured scene: the approaches that are appropriate for long-range scenes, and the approaches that are suitable for close-range scenes. In the literature, this subject of research is not well documented, especially for close-range surveillance application domains. Our research is focused on novel image registration solutions for both close-range and long-range scenes featuring multiple humans. The proposed solutions are presented in the four articles included in this thesis. Our registration methods are applicable for further video analysis such as tracking, human localization, behavioral pattern analysis, and object categorization. For far-range video surveillance, we propose an iterative system that consists of simultaneous thermal-visible video registration, sensor fusion, and people tracking. Our video registration is based on a RANSAC object trajectory matching, which estimates an affine transformation matrix to globally transform foreground objects of one image on another one. Our proposed multimodal surveillance system is based on a novel feedback scheme between registration and tracking modules that augments the performance of both modules iteratively over time. Our methods are designed for online applications and no camera calibration or special setup is required. For close-range video surveillance applications, we introduce Local Self-Similarity (LSS) as a viable similarity measure for matching corresponding human body regions of thermal and visible images. We also demonstrate theoretically and quantitatively that LSS, as a thermal-visible similarity measure, is more robust to differences between corresponding regions' textures than the Mutual Information (MI), which is the classic multimodal similarity measure. Other viable local image descriptors including Histogram Of Gradient (HOG), Scale Invariant Feature Transform (SIFT), and Binary Robust Independent Elementary Feature (BRIEF) are also outperformed by LSS. Moreover, we propose a LSS-based dense local stereo correspondence algorithm based on a voting approach, which estimates a dense disparity map for each foreground region in the image. The resulting disparity map can then be used to align the reference image on the second image. We demonstrate that our proposed LSS-based local registration method outperforms similar state-of-the-art MI-based local registration methods in the literature. Our experiments were carried out using realistic human monitoring scenarios in a close-range scene. Due to the shortcomings of local stereo correspondence approaches for estimating accurate disparities in depth discontinuity regions, we propose a novel stereo correspondence method based on a global optimization approach. We introduce a stereo model appropriate for thermal-visible image registration using an energy minimization framework and Belief Propagation (BP) as a method to optimize the disparity assignment via an energy function. In this method, we integrated color and motion visual cues as a soft constraint into an energy function to improve disparity assignment accuracy in depth discontinuities. Although global correspondence approaches are computationally more expensive compared to Winner Take All (WTA) local correspondence approaches, the efficient BP algorithm and parallel processing programming (openMP) in C++ that we used in our implementation, speed up the processing time significantly and make our methods viable for video surveillance applications. Our methods are implemented in C++ using OpenCV library and object-oriented programming. Our methods are designed to be integrated easily for further video analysis. In other words, the input data of our methods could come from two synchronized online video streams. For further analysis a new module could be added in our frame-by-frame algorithmic diagram. Further analysis might be object tracking, human localization, and trajectory pattern analysis for multimodal long-range monitoring applications, and behavior pattern analysis, object categorization, and tracking for close-range applications

Soft Biometric Analysis: MultiPerson and RealTime Pedestrian Attribute Recognition in Crowded Urban Environments

Traditionally, recognition systems were only based on human hard biometrics. However, the ubiquitous CCTV cameras have raised the desire to analyze human biometrics from far distances, without people attendance in the acquisition process. Highresolution face closeshots are rarely available at far distances such that facebased systems cannot provide reliable results in surveillance applications. Human soft biometrics such as body and clothing attributes are believed to be more effective in analyzing human data collected by security cameras. This thesis contributes to the human soft biometric analysis in uncontrolled environments and mainly focuses on two tasks: Pedestrian Attribute Recognition (PAR) and person reidentification (reid). We first review the literature of both tasks and highlight the history of advancements, recent developments, and the existing benchmarks. PAR and person reid difficulties are due to significant distances between intraclass samples, which originate from variations in several factors such as body pose, illumination, background, occlusion, and data resolution. Recent stateoftheart approaches present endtoend models that can extract discriminative and comprehensive feature representations from people. The correlation between different regions of the body and dealing with limited learning data is also the objective of many recent works. Moreover, class imbalance and correlation between human attributes are specific challenges associated with the PAR problem. We collect a large surveillance dataset to train a novel gender recognition model suitable for uncontrolled environments. We propose a deep residual network that extracts several posewise patches from samples and obtains a comprehensive feature representation. In the next step, we develop a model for multiple attribute recognition at once. Considering the correlation between human semantic attributes and class imbalance, we respectively use a multitask model and a weighted loss function. We also propose a multiplication layer on top of the backbone features extraction layers to exclude the background features from the final representation of samples and draw the attention of the model to the foreground area. We address the problem of person reid by implicitly defining the receptive fields of deep learning classification frameworks. The receptive fields of deep learning models determine the most significant regions of the input data for providing correct decisions. Therefore, we synthesize a set of learning data in which the destructive regions (e.g., background) in each pair of instances are interchanged. A segmentation module determines destructive and useful regions in each sample, and the label of synthesized instances are inherited from the sample that shared the useful regions in the synthesized image. The synthesized learning data are then used in the learning phase and help the model rapidly learn that the identity and background regions are not correlated. Meanwhile, the proposed solution could be seen as a data augmentation approach that fully preserves the label information and is compatible with other data augmentation techniques. When reid methods are learned in scenarios where the target person appears with identical garments in the gallery, the visual appearance of clothes is given the most importance in the final feature representation. Clothbased representations are not reliable in the longterm reid settings as people may change their clothes. Therefore, developing solutions that ignore clothing cues and focus on identityrelevant features are in demand. We transform the original data such that the identityrelevant information of people (e.g., face and body shape) are removed, while the identityunrelated cues (i.e., color and texture of clothes) remain unchanged. A learned model on the synthesized dataset predicts the identityunrelated cues (shortterm features). Therefore, we train a second model coupled with the first model and learns the embeddings of the original data such that the similarity between the embeddings of the original and synthesized data is minimized. This way, the second model predicts based on the identityrelated (longterm) representation of people. To evaluate the performance of the proposed models, we use PAR and person reid datasets, namely BIODI, PETA, RAP, Market1501, MSMTV2, PRCC, LTCC, and MIT and compared our experimental results with stateoftheart methods in the field. In conclusion, the data collected from surveillance cameras have low resolution, such that the extraction of hard biometric features is not possible, and facebased approaches produce poor results. In contrast, soft biometrics are robust to variations in data quality. So, we propose approaches both for PAR and person reid to learn discriminative features from each instance and evaluate our proposed solutions on several publicly available benchmarks.This thesis was prepared at the University of Beria Interior, IT Instituto de Telecomunicações, Soft Computing and Image Analysis Laboratory (SOCIA Lab), Covilhã Delegation, and was submitted to the University of Beira Interior for defense in a public examination session

Human Movement Analysis: Ballistic Dynamics, and Edge Continuity for Pose Estimation

We present two contributions to human movement analysis: (a) a ballistic dynamical model for recognizing movements, and (b) a model for coupling edge continuity with contour matching. We describe a Bayesian approach for visual analysis of ballistic hand movements, namely reaches and strikes. These movements are most commonly used for interacting with objects and the environment. One of the key challenges to recognizing them is the variability of the target-location of the hand~- people can reach above their heads, for something on the floor, etc. Our approach recognizes them independent of the movement's target-location and direction by modelling the ballistic dynamics. A video sequence is automatically segmented into ballistic subsequences without tracking the hands. The segments are then classified into strike and reach movements based on low-level motion features. Each ballistic segment is further analyzed to compute qualitative labels for the movement's target-location and direction. Tests are presented with a set of reach and strike movement sequences. We present an approach for whole-body pose contour matching. Contour matching in natural images in the absence of foreground-background segmentation is difficult. Usually an asymmetric approach is adopted, where a contour is said to match well if it aligns with a subset of the image's gradients. This leads to problems as the contour can match with a portion of an object's outline and ignore the remainder. We present a model for using edge-continuity to address this issue. Pairs of edge elements in the image are linked with affinities if they are likely to belong to the same object. A contour that matches with a set of image gradients is constrained to also match with other gradients having high affinities with the chosen ones. A Markov Random Field framework is employed to couple edge continuity and contour matching into a joint optimization process. The approach is illustrated with applications to pose estimation and human detection

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Spatial and temporal background modelling of non-stationary visual scenes

PhDThe prevalence of electronic imaging systems in everyday life has become increasingly apparent in recent years. Applications are to be found in medical scanning, automated manufacture, and perhaps most significantly, surveillance. Metropolitan areas, shopping malls, and road traffic management all employ and benefit from an unprecedented quantity of video cameras for monitoring purposes. But the high cost and limited effectiveness of employing humans as the final link in the monitoring chain has driven scientists to seek solutions based on machine vision techniques. Whilst the field of machine vision has enjoyed consistent rapid development in the last 20 years, some of the most fundamental issues still remain to be solved in a satisfactory manner. Central to a great many vision applications is the concept of segmentation, and in particular, most practical systems perform background subtraction as one of the first stages of video processing. This involves separation of ‘interesting foreground’ from the less informative but persistent background. But the definition of what is ‘interesting’ is somewhat subjective, and liable to be application specific. Furthermore, the background may be interpreted as including the visual appearance of normal activity of any agents present in the scene, human or otherwise. Thus a background model might be called upon to absorb lighting changes, moving trees and foliage, or normal traffic flow and pedestrian activity, in order to effect what might be termed in ‘biologically-inspired’ vision as pre-attentive selection. This challenge is one of the Holy Grails of the computer vision field, and consequently the subject has received considerable attention. This thesis sets out to address some of the limitations of contemporary methods of background segmentation by investigating methods of inducing local mutual support amongst pixels in three starkly contrasting paradigms: (1) locality in the spatial domain, (2) locality in the shortterm time domain, and (3) locality in the domain of cyclic repetition frequency. Conventional per pixel models, such as those based on Gaussian Mixture Models, offer no spatial support between adjacent pixels at all. At the other extreme, eigenspace models impose a structure in which every image pixel bears the same relation to every other pixel. But Markov Random Fields permit definition of arbitrary local cliques by construction of a suitable graph, and 3 are used here to facilitate a novel structure capable of exploiting probabilistic local cooccurrence of adjacent Local Binary Patterns. The result is a method exhibiting strong sensitivity to multiple learned local pattern hypotheses, whilst relying solely on monochrome image data. Many background models enforce temporal consistency constraints on a pixel in attempt to confirm background membership before being accepted as part of the model, and typically some control over this process is exercised by a learning rate parameter. But in busy scenes, a true background pixel may be visible for a relatively small fraction of the time and in a temporally fragmented fashion, thus hindering such background acquisition. However, support in terms of temporal locality may still be achieved by using Combinatorial Optimization to derive shortterm background estimates which induce a similar consistency, but are considerably more robust to disturbance. A novel technique is presented here in which the short-term estimates act as ‘pre-filtered’ data from which a far more compact eigen-background may be constructed. Many scenes entail elements exhibiting repetitive periodic behaviour. Some road junctions employing traffic signals are among these, yet little is to be found amongst the literature regarding the explicit modelling of such periodic processes in a scene. Previous work focussing on gait recognition has demonstrated approaches based on recurrence of self-similarity by which local periodicity may be identified. The present work harnesses and extends this method in order to characterize scenes displaying multiple distinct periodicities by building a spatio-temporal model. The model may then be used to highlight abnormality in scene activity. Furthermore, a Phase Locked Loop technique with a novel phase detector is detailed, enabling such a model to maintain correct synchronization with scene activity in spite of noise and drift of periodicity. This thesis contends that these three approaches are all manifestations of the same broad underlying concept: local support in each of the space, time and frequency domains, and furthermore, that the support can be harnessed practically, as will be demonstrated experimentally