Person Re-identification by Local Maximal Occurrence Representation and Metric Learning

Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and extracted features please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda

Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy

In this paper we shall consider the problem of deploying attention to subsets of the video streams for collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer's attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream/camera, detects interesting objects or activities, or switches to a more profitable stream. The approach proposed here is suitable to be exploited for multi-stream video summarization. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g. activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Dataset, a publicly available dataset, are presented to illustrate the utility of the proposed technique.Comment: Accepted to IEEE Transactions on Image Processin

A neurobiological and computational analysis of target discrimination in visual clutter by the insect visual system.

Some insects have the capability to detect and track small moving objects, often against cluttered moving backgrounds. Determining how this task is performed is an intriguing challenge, both from a physiological and computational perspective. Previous research has characterized higher-order neurons within the fly brain known as 'small target motion detectors‘ (STMD) that respond selectively to targets, even within complex moving surrounds. Interestingly, these cells still respond robustly when the velocity of the target is matched to the velocity of the background (i.e. with no relative motion cues). We performed intracellular recordings from intermediate-order neurons in the fly visual system (the medulla). These full-wave rectifying, transient cells (RTC) reveal independent adaptation to luminance changes of opposite signs (suggesting separate 'on‘ and 'off‘ channels) and fast adaptive temporal mechanisms (as seen in some previously described cell types). We show, via electrophysiological experiments, that the RTC is temporally responsive to rapidly changing stimuli and is well suited to serving an important function in a proposed target-detecting pathway. To model this target discrimination, we use high dynamic range (HDR) natural images to represent 'real-world‘ luminance values that serve as inputs to a biomimetic representation of photoreceptor processing. Adaptive spatiotemporal high-pass filtering (1st-order interneurons) shapes the transient 'edge-like‘ responses, useful for feature discrimination. Following this, a model for the RTC implements a nonlinear facilitation between the rapidly adapting, and independent polarity contrast channels, each with centre-surround antagonism. The recombination of the channels results in increased discrimination of small targets, of approximately the size of a single pixel, without the need for relative motion cues. This method of feature discrimination contrasts with traditional target and background motion-field computations. We show that our RTC-based target detection model is well matched to properties described for the higher-order STMD neurons, such as contrast sensitivity, height tuning and velocity tuning. The model output shows that the spatiotemporal profile of small targets is sufficiently rare within natural scene imagery to allow our highly nonlinear 'matched filter‘ to successfully detect many targets from the background. The model produces robust target discrimination across a biologically plausible range of target sizes and a range of velocities. We show that the model for small target motion detection is highly correlated to the velocity of the stimulus but not other background statistics, such as local brightness or local contrast, which normally influence target detection tasks. From an engineering perspective, we examine model elaborations for improved target discrimination via inhibitory interactions from correlation-type motion detectors, using a form of antagonism between our feature correlator and the more typical motion correlator. We also observe that a changing optimal threshold is highly correlated to the value of observer ego-motion. We present an elaborated target detection model that allows for implementation of a static optimal threshold, by scaling the target discrimination mechanism with a model-derived velocity estimation of ego-motion. Finally, we investigate the physiological relevance of this target discrimination model. We show that via very subtle image manipulation of the visual stimulus, our model accurately predicts dramatic changes in observed electrophysiological responses from STMD neurons.Thesis (Ph.D.) - University of Adelaide, School of Molecular and Biomedical Science, 200

Biologically motivated keypoint detection for RGB-D data

With the emerging interest in active vision, computer vision researchers have been increasingly concerned with the mechanisms of attention. Therefore, several visual attention computational models inspired by the human visual system, have been developed, aiming at the detection of regions of interest in images. This thesis is focused on selective visual attention, which provides a mechanism for the brain to focus computational resources on an object at a time, guided by low-level image properties (Bottom-Up attention). The task of recognizing objects in different locations is achieved by focusing on different locations, one at a time. Given the computational requirements of the models proposed, the research in this area has been mainly of theoretical interest. More recently, psychologists, neurobiologists and engineers have developed cooperation's and this has resulted in considerable benefits. The first objective of this doctoral work is to bring together concepts and ideas from these different research areas, providing a study of the biological research on human visual system and a discussion of the interdisciplinary knowledge in this area, as well as the state-of-art on computational models of visual attention (bottom-up). Normally, the visual attention is referred by engineers as saliency: when people fix their look in a particular region of the image, that's because that region is salient. In this research work, saliency methods are presented based on their classification (biological plausible, computational or hybrid) and in a chronological order. A few salient structures can be used for applications like object registration, retrieval or data simplification, being possible to consider these few salient structures as keypoints when aiming at performing object recognition. Generally, object recognition algorithms use a large number of descriptors extracted in a dense set of points, which comes along with very high computational cost, preventing real-time processing. To avoid the problem of the computational complexity required, the features have to be extracted from a small set of points, usually called keypoints. The use of keypoint-based detectors allows the reduction of the processing time and the redundancy in the data. Local descriptors extracted from images have been extensively reported in the computer vision literature. Since there is a large set of keypoint detectors, this suggests the need of a comparative evaluation between them. In this way, we propose to do a description of 2D and 3D keypoint detectors, 3D descriptors and an evaluation of existing 3D keypoint detectors in a public available point cloud library with 3D real objects. The invariance of the 3D keypoint detectors was evaluated according to rotations, scale changes and translations. This evaluation reports the robustness of a particular detector for changes of point-of-view and the criteria used are the absolute and the relative repeatability rate. In our experiments, the method that achieved better repeatability rate was the ISS3D method. The analysis of the human visual system and saliency maps detectors with biological inspiration led to the idea of making an extension for a keypoint detector based on the color information in the retina. Such proposal produced a 2D keypoint detector inspired by the behavior of the early visual system. Our method is a color extension of the BIMP keypoint detector, where we include both color and intensity channels of an image: color information is included in a biological plausible way and multi-scale image features are combined into a single keypoints map. This detector is compared against state-of-art detectors and found particularly well-suited for tasks such as category and object recognition. The recognition process is performed by comparing the extracted 3D descriptors in the locations indicated by the keypoints after mapping the 2D keypoints locations to the 3D space. The evaluation allowed us to obtain the best pair keypoint detector/descriptor on a RGB-D object dataset. Using our keypoint detector and the SHOTCOLOR descriptor a good category recognition rate and object recognition rate were obtained, and it is with the PFHRGB descriptor that we obtain the best results. A 3D recognition system involves the choice of keypoint detector and descriptor. A new method for the detection of 3D keypoints on point clouds is presented and a benchmarking is performed between each pair of 3D keypoint detector and 3D descriptor to evaluate their performance on object and category recognition. These evaluations are done in a public database of real 3D objects. Our keypoint detector is inspired by the behavior and neural architecture of the primate visual system: the 3D keypoints are extracted based on a bottom-up 3D saliency map, which is a map that encodes the saliency of objects in the visual environment. The saliency map is determined by computing conspicuity maps (a combination across different modalities) of the orientation, intensity and color information, in a bottom-up and in a purely stimulusdriven manner. These three conspicuity maps are fused into a 3D saliency map and, finally, the focus of attention (or "keypoint location") is sequentially directed to the most salient points in this map. Inhibiting this location automatically allows the system to attend to the next most salient location. The main conclusions are: with a similar average number of keypoints, our 3D keypoint detector outperforms the other eight 3D keypoint detectors evaluated by achiving the best result in 32 of the evaluated metrics in the category and object recognition experiments, when the second best detector only obtained the best result in 8 of these metrics. The unique drawback is the computational time, since BIK-BUS is slower than the other detectors. Given that differences are big in terms of recognition performance, size and time requirements, the selection of the keypoint detector and descriptor has to be matched to the desired task and we give some directions to facilitate this choice. After proposing the 3D keypoint detector, the research focused on a robust detection and tracking method for 3D objects by using keypoint information in a particle filter. This method consists of three distinct steps: Segmentation, Tracking Initialization and Tracking. The segmentation is made to remove all the background information, reducing the number of points for further processing. In the initialization, we use a keypoint detector with biological inspiration. The information of the object that we want to follow is given by the extracted keypoints. The particle filter does the tracking of the keypoints, so with that we can predict where the keypoints will be in the next frame. In a recognition system, one of the problems is the computational cost of keypoint detectors with this we intend to solve this problem. The experiments with PFBIKTracking method are done indoors in an office/home environment, where personal robots are expected to operate. The Tracking Error evaluates the stability of the general tracking method. We also quantitatively evaluate this method using a "Tracking Error". Our evaluation is done by the computation of the keypoint and particle centroid. Comparing our system that the tracking method which exists in the Point Cloud Library, we archive better results, with a much smaller number of points and computational time. Our method is faster and more robust to occlusion when compared to the OpenniTracker.Com o interesse emergente na visão ativa, os investigadores de visão computacional têm estado cada vez mais preocupados com os mecanismos de atenção. Por isso, uma série de modelos computacionais de atenção visual, inspirado no sistema visual humano, têm sido desenvolvidos. Esses modelos têm como objetivo detetar regiões de interesse nas imagens. Esta tese está focada na atenção visual seletiva, que fornece um mecanismo para que o cérebro concentre os recursos computacionais num objeto de cada vez, guiado pelas propriedades de baixo nível da imagem (atenção Bottom-Up). A tarefa de reconhecimento de objetos em diferentes locais é conseguida através da concentração em diferentes locais, um de cada vez. Dados os requisitos computacionais dos modelos propostos, a investigação nesta área tem sido principalmente de interesse teórico. Mais recentemente, psicólogos, neurobiólogos e engenheiros desenvolveram cooperações e isso resultou em benefícios consideráveis. No início deste trabalho, o objetivo é reunir os conceitos e ideias a partir dessas diferentes áreas de investigação. Desta forma, é fornecido o estudo sobre a investigação da biologia do sistema visual humano e uma discussão sobre o conhecimento interdisciplinar da matéria, bem como um estado de arte dos modelos computacionais de atenção visual (bottom-up). Normalmente, a atenção visual é denominada pelos engenheiros como saliência, se as pessoas fixam o olhar numa determinada região da imagem é porque esta região é saliente. Neste trabalho de investigação, os métodos saliência são apresentados em função da sua classificação (biologicamente plausível, computacional ou híbrido) e numa ordem cronológica. Algumas estruturas salientes podem ser usadas, em vez do objeto todo, em aplicações tais como registo de objetos, recuperação ou simplificação de dados. É possível considerar estas poucas estruturas salientes como pontos-chave, com o objetivo de executar o reconhecimento de objetos. De um modo geral, os algoritmos de reconhecimento de objetos utilizam um grande número de descritores extraídos num denso conjunto de pontos. Com isso, estes têm um custo computacional muito elevado, impedindo que o processamento seja realizado em tempo real. A fim de evitar o problema da complexidade computacional requerido, as características devem ser extraídas a partir de um pequeno conjunto de pontos, geralmente chamados pontoschave. O uso de detetores de pontos-chave permite a redução do tempo de processamento e a quantidade de redundância dos dados. Os descritores locais extraídos a partir das imagens têm sido amplamente reportados na literatura de visão por computador. Uma vez que existe um grande conjunto de detetores de pontos-chave, sugere a necessidade de uma avaliação comparativa entre eles. Desta forma, propomos a fazer uma descrição dos detetores de pontos-chave 2D e 3D, dos descritores 3D e uma avaliação dos detetores de pontos-chave 3D existentes numa biblioteca de pública disponível e com objetos 3D reais. A invariância dos detetores de pontoschave 3D foi avaliada de acordo com variações nas rotações, mudanças de escala e translações. Essa avaliação retrata a robustez de um determinado detetor no que diz respeito às mudanças de ponto-de-vista e os critérios utilizados são as taxas de repetibilidade absoluta e relativa. Nas experiências realizadas, o método que apresentou melhor taxa de repetibilidade foi o método ISS3D. Com a análise do sistema visual humano e dos detetores de mapas de saliência com inspiração biológica, surgiu a ideia de se fazer uma extensão para um detetor de ponto-chave com base na informação de cor na retina. A proposta produziu um detetor de ponto-chave 2D inspirado pelo comportamento do sistema visual. O nosso método é uma extensão com base na cor do detetor de ponto-chave BIMP, onde se incluem os canais de cor e de intensidade de uma imagem. A informação de cor é incluída de forma biológica plausível e as características multi-escala da imagem são combinadas num único mapas de pontos-chave. Este detetor é comparado com os detetores de estado-da-arte e é particularmente adequado para tarefas como o reconhecimento de categorias e de objetos. O processo de reconhecimento é realizado comparando os descritores 3D extraídos nos locais indicados pelos pontos-chave. Para isso, as localizações do pontos-chave 2D têm de ser convertido para o espaço 3D. Isto foi possível porque o conjunto de dados usado contém a localização de cada ponto de no espaço 2D e 3D. A avaliação permitiu-nos obter o melhor par detetor de ponto-chave/descritor num RGB-D object dataset. Usando o nosso detetor de ponto-chave e o descritor SHOTCOLOR, obtemos uma noa taxa de reconhecimento de categorias e para o reconhecimento de objetos é com o descritor PFHRGB que obtemos os melhores resultados. Um sistema de reconhecimento 3D envolve a escolha de detetor de ponto-chave e descritor, por isso é apresentado um novo método para a deteção de pontos-chave em nuvens de pontos 3D e uma análise comparativa é realizada entre cada par de detetor de ponto-chave 3D e descritor 3D para avaliar o desempenho no reconhecimento de categorias e de objetos. Estas avaliações são feitas numa base de dados pública de objetos 3D reais. O nosso detetor de ponto-chave é inspirado no comportamento e na arquitetura neural do sistema visual dos primatas. Os pontos-chave 3D são extraídas com base num mapa de saliências 3D bottom-up, ou seja, um mapa que codifica a saliência dos objetos no ambiente visual. O mapa de saliência é determinada pelo cálculo dos mapas de conspicuidade (uma combinação entre diferentes modalidades) da orientação, intensidade e informações de cor de forma bottom-up e puramente orientada para o estímulo. Estes três mapas de conspicuidade são fundidos num mapa de saliência 3D e, finalmente, o foco de atenção (ou "localização do ponto-chave") está sequencialmente direcionado para os pontos mais salientes deste mapa. Inibir este local permite que o sistema automaticamente orientado para próximo local mais saliente. As principais conclusões são: com um número médio similar de pontos-chave, o nosso detetor de ponto-chave 3D supera os outros oito detetores de pontos-chave 3D avaliados, obtendo o melhor resultado em 32 das métricas avaliadas nas experiências do reconhecimento das categorias e dos objetos, quando o segundo melhor detetor obteve apenas o melhor resultado em 8 dessas métricas. A única desvantagem é o tempo computacional, uma vez que BIK-BUS é mais lento do que os outros detetores. Dado que existem grandes diferenças em termos de desempenho no reconhecimento, de tamanho e de tempo, a seleção do detetor de ponto-chave e descritor tem de ser interligada com a tarefa desejada e nós damos algumas orientações para facilitar esta escolha neste trabalho de investigação. Depois de propor um detetor de ponto-chave 3D, a investigação incidiu sobre um método robusto de deteção e tracking de objetos 3D usando as informações dos pontos-chave num filtro de partículas. Este método consiste em três etapas distintas: Segmentação, Inicialização do Tracking e Tracking. A segmentação é feita de modo a remover toda a informação de fundo, a fim de reduzir o número de pontos para processamento futuro. Na inicialização, usamos um detetor de ponto-chave com inspiração biológica. A informação do objeto que queremos seguir é dada pelos pontos-chave extraídos. O filtro de partículas faz o acompanhamento dos pontoschave, de modo a se poder prever onde os pontos-chave estarão no próximo frame. As experiências com método PFBIK-Tracking são feitas no interior, num ambiente de escritório/casa, onde se espera que robôs pessoais possam operar. Também avaliado quantitativamente este método utilizando um "Tracking Error". A avaliação passa pelo cálculo das centróides dos pontos-chave e das partículas. Comparando o nosso sistema com o método de tracking que existe na biblioteca usada no desenvolvimento, nós obtemos melhores resultados, com um número muito menor de pontos e custo computacional. O nosso método é mais rápido e mais robusto em termos de oclusão, quando comparado com o OpenniTracker

Multi camera visual saliency using image stitching

This paper presents and investigates two models for a multi camera configuration with visual saliency capability. Applications in various imaging fields each have a different set of detection parameters and requirements which would result in the necessity of software changes. The visual saliency capability offered by this multi camera model allows generic detection of conspicuous objects be it human or nonhuman based on simple low level features. As multiple cameras are used, an image stitching technique is employed to allow combination of Field-of-View (FoV) from different camera captures to provide a panoramic detection field. The stitching technique is also used to complement the visual saliency model in this work. In the first model, image stitching is applied to individual captures to provide a wider FoV, whereby the visual saliency algorithm would able to operate on a wide area. For the second model, visual saliency is applied to individual captures. Then, the maps are recombined based on a set of stitching parameters to reinforced salient features present in objects at the FoV overlap regions. Simulations of the two models are conducted and demonstrated for performance evaluation

On the relationship between optical variability, visual saliency, and eye fixations: a computational approach

A hierarchical definition of optical variability is proposed that links physical magnitudes to visual saliency and yields a more reductionist interpretation than previous approaches. This definition is shown to be grounded on the classical efficient coding hypothesis. Moreover, we propose that a major goal of contextual adaptation mechanisms is to ensure the invariance of the behavior that the contribution of an image point to optical variability elicits in the visual system. This hypothesis and the necessary assumptions are tested through the comparison with human fixations and state-of-the-art approaches to saliency in three open access eye-tracking datasets, including one devoted to images with faces, as well as in a novel experiment using hyperspectral representations of surface reflectance. The results on faces yield a significant reduction of the potential strength of semantic influences compared to previous works. The results on hyperspectral images support the assumptions to estimate optical variability. As well, the proposed approach explains quantitative results related to a visual illusion observed for images of corners, which does not involve eye movementsS

Feature combination strategies for saliency-based visual attention systems

Bottom-up or saliency-based visual attention allows primates to detect nonspecific conspicuous targets in cluttered scenes. A classical metaphor, derived from electrophysiological and psychophysical studies, describes attention as a rapidly shiftable “spotlight.” We use a model that reproduces the attentional scan paths of this spotlight. Simple multi-scale “feature maps” detect local spatial discontinuities in intensity, color, and orientation, and are combined into a unique “master” or “saliency” map. The saliency map is sequentially scanned, in order of decreasing saliency, by the focus of attention. We here study the problem of combining feature maps, from different visual modalities (such as color and orientation), into a unique saliency map. Four combination strategies are compared using three databases of natural color images: (1) Simple normalized summation, (2) linear combination with learned weights, (3) global nonlinear normalization followed by summation, and (4) local nonlinear competition between salient locations followed by summation. Performance was measured as the number of false detections before the most salient target was found. Strategy (1) always yielded poorest performance and (2) best performance, with a threefold to eightfold improvement in time to find a salient target. However, (2) yielded specialized systems with poor generalization. Interestingly, strategy (4) and its simplified, computationally efficient approximation (3) yielded significantly better performance than (1), with up to fourfold improvement, while preserving generality

Event-driven visual attention for the humanoid robot iCub.

Fast reaction to sudden and potentially interesting stimuli is a crucial feature for safe and reliable interaction with the environment. Here we present a biologically inspired attention system developed for the humanoid robot iCub. It is based on input from unconventional event-driven vision sensors and an efficient computational method. The resulting system shows low-latency and fast determination of the location of the focus of attention. The performance is benchmarked against an instance of the state of the art in robotics artificial attention system used in robotics. Results show that the proposed system is two orders of magnitude faster that the benchmark in selecting a new stimulus to attend

A Multi-scale colour and Keypoint Density-based Approach for Visual Saliency Detection.

In the first seconds of observation of an image, several visual attention processes are involved in the identification of the visual targets that pop-out from the scene to our eyes. Saliency is the quality that makes certain regions of an image stand out from the visual field and grab our attention. Saliency detection models, inspired by visual cortex mechanisms, employ both colour and luminance features. Furthermore, both locations of pixels and presence of objects influence the Visual Attention processes. In this paper, we propose a new saliency method based on the combination of the distribution of interest points in the image with multiscale analysis, a centre bias module and a machine learning approach. We use perceptually uniform colour spaces to study how colour impacts on the extraction of saliency. To investigate eye-movements and assess the performances of saliency methods over object-based images, we conduct experimental sessions on our dataset ETTO (Eye Tracking Through Objects). Experiments show our approach to be accurate in the detection of saliency concerning state-of-the-art methods and accessible eye-movement datasets. The performances over object-based images are excellent and consistent on generic pictures. Besides, our work reveals interesting findings on some relationships between saliency and perceptually uniform colour spaces