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Resource-constrained re-identification in camera networksTahir, Syed Fahad January 2016 (has links)
In multi-camera surveillance, association of people detected in different camera views over time, known as person re-identification, is a fundamental task. Re-identification is a challenging problem because of changes in the appearance of people under varying camera conditions. Existing approaches focus on improving the re-identification accuracy, while no specific effort has yet been put into efficiently utilising the available resources that are normally limited in a camera network, such as storage, computation and communication capabilities. In this thesis, we aim to perform and improve the task of re-identification under constrained resources. More specifically, we reduce the data needed to represent the appearance of an object through a proposed feature selection method and a difference-vector representation method. The proposed feature-selection method considers the computational cost of feature extraction and the cost of storing the feature descriptor jointly with the feature's re-identification performance to select the most cost-effective and well-performing features. This selection allows us to improve inter-camera re-identification while reducing storage and computation requirements within each camera. The selected features are ranked in the order of effectiveness, which enable a further reduction by dropping the least effective features when application constraints require this conformity. We also reduce the communication overhead in the camera network by transferring only a difference vector, obtained from the extracted features of an object and the reference features within a camera, as an object representation for the association. In order to reduce the number of possible matches per association, we group the objects appearing within a defined time-interval in un-calibrated camera pairs. Such a grouping improves the re-identification, since only those objects that appear within the same time-interval in a camera pair are needed to be associated. For temporal alignment of cameras, we exploit differences between the frame numbers of the detected objects in a camera pair. Finally, in contrast to pairwise camera associations used in literature, we propose a many-to-one camera association method for re-identification, where multiple cameras can be candidates for having generated the previous detections of an object. We obtain camera-invariant matching scores from the scores obtained using the pairwise re-identification approaches. These scores measure the chances of a correct match between the objects detected in a group of cameras. Experimental results on publicly available and in-lab multi-camera image and video datasets show that the proposed methods successfully reduce storage, computation and communication requirements while improving the re-identification rate compared to existing re-identification approaches.
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Releasing Recommendation Datasets while Preserving PrivacySomasundaram, Jyothilakshmi 26 May 2011 (has links)
No description available.
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PERSON RE-IDENTIFICATION & VIDEO-BASED HEART RATE ESTIMATIONDahjung Chung (7030574) 13 August 2019 (has links)
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<p>Estimation of physiological vital signs such as the Heart Rate (HR) has attracted a
lot of attention due to the increase interest in health monitoring. The most common
HR estimation methods such as Photoplethysmography(PPG) require the physical
contact with the subject and limit the movement of the subject. Video-based HR estimation, known as videoplethysmography (VHR), uses image/video processing techniques to estimate remotely the human HR. Even though various VHR methods have
been proposed over the past 5 years, there are still challenging problems such as diverse skin tone and motion artifacts. In this thesis we present a VHR method using
temporal difference filtering and small variation amplification based on the assumption that HR is the small color variations of skin, i.e. micro blushing. This method is
evaluated and compared with the two previous VHR methods. Additionally, we propose the use of spatial pruning for an alternative of skin detection and homomorphic
filtering for the motion artifact compensation.
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<p>Intelligent video surveillance system is a crucial tool for public safety. One of
the goals is to extract meaningful information efficiently from the large volume of
surveillance videos. Person re-identification (ReID) is a fundamental task associated
with intelligent video surveillance system. For example, ReID can be used to identity
the person of interest to help law enforcement when they re-appear in the different
cameras at different time. ReID can be formally defined as establishing the correspondence between images of a person taken from different cameras. Even though
ReID has been intensively studied over the past years, it is still an active research
area due to various challenges such as illumination variations, occlusions, view point changes and the lack of data. In this thesis we propose a weighted two stream train-
ing objective function which combines the Siamese cost of the spatial and temporal
streams with the objective of predicting a person’s identity. Additionally, we present
a camera-aware image-to-image translation method using similarity preserving Star-
GAN (SP-StarGAN) as the data augmentation for ReID. We evaluate our proposed
methods on the publicly available datasets and demonstrate the efficacy of our methods.</p></div></div></div>
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Minimising human annotation for scalable person re-identificationWang, Hanxiao January 2017 (has links)
Among the diverse tasks performed by an intelligent distributed multi-camera surveillance system, person re-identification (re-id) is one of the most essential. Re-id refers to associating an individual or a group of people across non-overlapping cameras at different times and locations, and forms the foundation of a variety of applications ranging from security and forensic search to quotidian retail and health care. Though attracted rapidly increasing academic interests over the past decade, it still remains a non-trivial and unsolved problem for launching a practical reid system in real-world environments, due to the ambiguous and noisy feature of surveillance data and the potentially dramatic visual appearance changes caused by uncontrolled variations in human poses and divergent viewing conditions across distributed camera views. To mitigate such visual ambiguity and appearance variations, most existing re-id approaches rely on constructing fully supervised machine learning models with extensively labelled training datasets which is unscalable for practical applications in the real-world. Particularly, human annotators must exhaustively search over a vast quantity of offline collected data, manually label cross-view matched images of a large population between every possible camera pair. Nonetheless, having the prohibitively expensive human efforts dissipated, a trained re-id model is often not easily generalisable and transferable, due to the elastic and dynamic operating conditions of a surveillance system. With such motivations, this thesis proposes several scalable re-id approaches with significantly reduced human supervision, readily applied to practical applications. More specifically, this thesis has developed and investigated four new approaches for reducing human labelling effort in real-world re-id as follows: Chapter 3 The first approach is affinity mining from unlabelled data. Different from most existing supervised approaches, this work aims to model the discriminative information for reid without exploiting human annotations, but from the vast amount of unlabelled person image data, thus applicable to both semi-supervised and unsupervised re-id. It is non-trivial since the human annotated identity matching correspondence is often the key to discriminative re-id modelling. In this chapter, an alternative strategy is explored by specifically mining two types of affinity relationships among unlabelled data: (1) inter-view data affinity and (2) intra-view data affinity. In particular, with such affinity information encoded as constraints, a Regularised Kernel Subspace Learning model is developed to explicitly reduce inter-view appearance variations and meanwhile enhance intra-view appearance disparity for more discriminative re-id matching. Consequently, annotation costs can be immensely alleviated and a scalable re-id model is readily to be leveraged to plenty of unlabelled data which is inexpensive to collect. Chapter 4 The second approach is saliency discovery from unlabelled data. This chapter continues to investigate the problem of what can be learned in unlabelled images without identity labels annotated by human. Other than affinity mining as proposed by Chapter 3, a different solution is proposed. That is, to discover localised visual appearance saliency of person appearances. Intuitively, salient and atypical appearances of human are able to uniquely and representatively describe and identify an individual, whilst also often robust to view changes and detection variances. Motivated by this, an unsupervised Generative Topic Saliency model is proposed to jointly perform foreground extraction, saliency detection, as well as discriminative re-id matching. This approach completely avoids the exhaustive annotation effort for model training, and thus better scales to real-world applications. Moreover, its automatically discovered re-id saliency representations are shown to be semantically interpretable, suitable for generating useful visual analysis for deployable user-oriented software tools. Chapter 5 The third approach is incremental learning from actively labelled data. Since learning from unlabelled data alone yields less discriminative matching results, and in some cases there will be limited human labelling resources available for re-id modelling, this chapter thus investigate the problem of how to maximise a model's discriminative capability with minimised labelling efforts. The challenges are to (1) automatically select the most representative data from a vast number of noisy/ambiguous unlabelled data in order to maximise model discrimination capacity; and (2) incrementally update the model parameters to accelerate machine responses and reduce human waiting time. To that end, this thesis proposes a regression based re-id model, characterised by its very fast and efficient incremental model updates. Furthermore, an effective active data sampling algorithm with three novel joint exploration-exploitation criteria is designed, to make automatic data selection feasible with notably reduced human labelling costs. Such an approach ensures annotations to be spent only on very few data samples which are most critical to model's generalisation capability, instead of being exhausted by blindly labelling many noisy and redundant training samples. Chapter 6 The last technical area of this thesis is human-in-the-loop learning from relevance feedback. Whilst former chapters mainly investigate techniques to reduce human supervision for model training, this chapter motivates a novel research area to further minimise human efforts spent in the re-id deployment stage. In real-world applications where camera network and potential gallery size increases dramatically, even the state-of-the-art re-id models generate much inferior re-id performances and human involvements at deployment stage is inevitable. To minimise such human efforts and maximise re-id performance, this thesis explores an alternative approach to re-id by formulating a hybrid human-computer learning paradigm with humans in the model matching loop. Specifically, a Human Verification Incremental Learning model is formulated which does not require any pre-labelled training data, therefore scalable to new camera pairs; Moreover, the proposed model learns cumulatively from human feedback to provide an instant improvement to re-id ranking of each probe on-the-fly, thus scalable to large gallery sizes. It has been demonstrated that the proposed re-id model achieves significantly superior re-id results whilst only consumes much less human supervision effort. For facilitating a holistic understanding about this thesis, the main studies are summarised and framed into a graphical abstract.
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Techniques d'optimisation pour la détection et ré-identification de personnes dans un réseau de caméras / Optimization techniques for people detection and re-identification in a camera networkBarbosa Anda, Francisco Rodolfo 10 December 2018 (has links)
Cette thèse traite de la détection et de la ré-identification de personnes dans un environnement instrumenté par un réseau de caméras à champ disjoint. Elle est à la confluence des communautés Recherche Opérationnelle et Vision car elle s'appuie sur des techniques d'optimisation combinatoire pour formaliser de nouvelles modalités de vision par ordinateur. Dans ce contexte, un détecteur visuel de personnes, basé sur la programmation linéaire en nombres entiers, est tout d'abord proposé. Son originalité est de prendre en compte le coût de traitement et non uniquement les performances de détection. Ce détecteur est évalué et comparé aux détecteurs de la littérature les plus performants. Ces expérimentations menées sur deux bases de données publiques mettent clairement en évidence l'intérêt de notre détecteur en terme de coût de traitement avec garantie de performance de détection. La seconde partie de la thèse porte sur la modalité de ré-identification de personnes. L'originalité de notre approche, dénommée D-NCR (pour Directed Network Consistent Re-identification), est de prendre explicitement en compte les temps minimum de transit des personnes dans le réseau de caméras et sa topologie pour améliorer la performance de la ré-identification. On montre que ce problème s'apparente à une recherche de chemins disjoints particuliers à profit maximum dans un graphe orienté. Un programme linéaire en nombres entiers est proposé pour sa modélisation et résolution. Les évaluations réalisées sur une base publique d'images sont prometteuses et montrent le potentiel de cette approche. / This thesis deals with people detection and re-identification in an environment instrumented by a network of disjoint-field cameras. It stands at the confluence of the Operational Research and Computer Vision communities as combinatorial optimization techniques are used to formalize new computer vision methods. In this context, a people visual detector, based on mixed-integer programming, is first propose that simultaneously take computation time and detection performances into account. This detector is evaluated and compared to the best detectors of the literature. These experiments, conducted on two public databases, clearly demonstrate the interest of our detector in terms of processing time with detection performance guarantee. The second part of the thesis deals with people re-identification. Our novel approach, called D-NCR (Directed Network Consistent Re-identification), explicitly takes minimum transit times in the camera network into account, as well as the network topology, in order to improve the re-identification performance. This problem is similar to the determination of particular maximum-profitable independent paths in an oriented graph. A mixed-integer program is proposed to model and solve this problem. The experiments made on a public dataset sound promising and tend to prove the potential of the approach.
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Software Systems for Large-Scale Retrospective Video AnalyticsTiantu Xu (10706787) 29 April 2021 (has links)
<p>Pervasive cameras are generating videos at an unprecedented pace, making videos the new frontier of big data. As the processors, e.g., CPU/GPU, become increasingly powerful, the cloud and edge nodes can generate useful insights from colossal video data. However, as the research in computer vision (CV) develops vigorously, the system area has been a blind spot in CV research. With colossal video data generated from cameras every day and limited compute resource budgets, how to design software systems to generate insights from video data efficiently?</p><p><br></p><p>Designing cost-efficient video analytics software systems is challenged by the expensive computation of vision operators, the colossal data volume, and the precious wireless bandwidth of surveillance cameras. To address above challenges, three software systems are proposed in this thesis. For the first system, we present VStore, a data store that supports fast, resource-efficient analytics over large archival videos. VStore manages video ingestion, storage, retrieval, and consumption and controls video formats through backward derivation of configuration: in the opposite direction along the video data path, VStore passes the video quantity and quality expected by analytics backward to retrieval, to storage, and to ingestion. VStore derives an optimal set of video formats, optimizes for different resources in a progressive manner, and runs queries as fast as 362x of video realtime. For the second system, we present a camera/cloud runtime called DIVA that supports querying cold videos distributed on low-cost wireless cameras. DIVA is built upon a novel zero-streaming paradigm: to save wireless bandwidth, when capturing video frames, a camera builds sparse yet accurate landmark frames without uploading any video data; when executing a query, a camera processes frames in multiple passes with increasingly more expensive operators. On diverse queries over 15 videos, DIVA runs at more than 100x realtime and outperforms competitive alternatives remarkably. For the third system, we present Clique, a practical object re-identification (ReID) engine that builds upon two unconventional techniques. First, Clique assesses target occurrences by clustering unreliable object features extracted by ReID algorithms, with each cluster representing the general impression of a distinct object to be matched against the input. Second, to search across camera videos, Clique samples cameras to maximize the spatiotemporal coverage and incrementally adds cameras for processing on demand. Through evaluation on 25 hours of traffic videos from 25 cameras, Clique reaches a high recall at 5 of 0.87 across 70 queries and runs at 830x of video realtime in achieving high accuracy.</p>
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Common-Near-Neighbor Information in Discriminative Spaces for Human Re-identification / 人物照合のための識別空間中での共通近傍情報Li, Wei 23 May 2014 (has links)
京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第18482号 / 情博第533号 / 新制||情||94(附属図書館) / 31360 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 美濃 導彦, 教授 河原 達也, 教授 中村 裕一 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM
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Reidentifikace objektů ve video streamu pomocí metod data analytics / Re-identification of Objects in Video Stream using Data AnalyticsSmrž, Dominik January 2021 (has links)
The wide usage of surveillance cameras provides data that can be used in various areas, such as security and urban planning. An important stepping stone for useful information extraction is matching the seen object across different points in time or different cameras. In this work, we focus specifically on this part of the video processing, usually referred to as re-identification. We split our work into two stages. In the first part, we focus on the spatial and temporal information regarding the detected objects. In the second part, we combine this metadata with the visual information. For the extraction of useful descriptors from the images, we use methods based on the color distribution as well as state-of-the-art deep neural networks. We also annotate a dataset to provide a comprehensive evaluation of our approaches. Additionally, we provide a custom tool we used to annotate the dataset. 1
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Person re-identification in images with deep learning / Ré-identification de personnes dans des images par apprentissage automatiqueChen, Yiqiang 12 October 2018 (has links)
La vidéosurveillance est d’une grande valeur pour la sécurité publique. En tant que l’un des plus importantes applications de vidéosurveillance, la ré-identification de personnes est définie comme le problème de l’identification d’individus dans des images captées par différentes caméras de surveillance à champs non-recouvrants. Cependant, cette tâche est difficile à cause d’une série de défis liés à l’apparence de la personne, tels que les variations de poses, de point de vue et de l’éclairage etc. Pour régler ces différents problèmes, dans cette thèse, nous proposons plusieurs approches basées sur l’apprentissage profond de sorte d’améliorer de différentes manières la performance de ré-identification. Dans la première approche, nous utilisons les attributs des piétons tels que genre, accessoires et vêtements. Nous proposons un système basé sur un réseau de neurones à convolution(CNN) qui est composé de deux branches : une pour la classification d’identité et l’autre pour la reconnaissance d’attributs. Nous fusionnons ensuite ces deux branches pour la ré-identification. Deuxièmement, nous proposons un CNN prenant en compte différentes orientations du corps humain. Le système fait une estimation de l’orientation et, de plus, combine les caractéristiques de différentes orientations extraites pour être plus robuste au changement de point de vue. Comme troisième contribution de cette thèse, nous proposons une nouvelle fonction de coût basée sur une liste d’exemples. Elle introduit une pondération basée sur le désordre du classement et permet d’optimiser directement les mesures d’évaluation. Enfin, pour un groupe de personnes, nous proposons d’extraire une représentation de caractéristiques visuelles invariante à la position d’un individu dans une image de group. Cette prise en compte de contexte de groupe réduit ainsi l’ambigüité de ré-identification. Pour chacune de ces quatre contributions, nous avons effectué de nombreuses expériences sur les différentes bases de données publiques pour montrer l’efficacité des approches proposées. / Video surveillance systems are of a great value for public safety. As one of the most import surveillance applications, person re-identification is defined as the problem of identifying people across images that have been captured by different surveillance cameras without overlapping fields of view. With the increasing need for automated video analysis, this task is increasingly receiving attention. However, this problem is challenging due to the large variations of lighting, pose, viewpoint and background. To tackle these different difficulties, in this thesis, we propose several deep learning based approaches to obtain a better person re-identification performance in different ways. In the first proposed approach, we use pedestrian attributes to enhance the person re-identification. The attributes are defined as semantic mid-level descriptions of persons, such as gender, accessories, clothing etc. They could be helpful to extract characteristics that are invariant to the pose and viewpoint variations thanks to the descriptor being on a higher semantic level. In order to make use of the attributes, we propose a CNN-based person re-identification framework composed of an identity classification branch and of an attribute recognition branch. At a later stage, these two cues are combined to perform person re-identification. Secondly, among the challenges, one of the most difficult is the variation under different viewpoint. The same person shows very different appearances from different points of view. To deal with this issue, we consider that the images under various orientations are from different domains. We propose an orientation-specific CNN. This framework performs body orientation regression in a gating branch, and in another branch learns separate orientation-specific layers as local experts. The combined orientation-specific CNN feature representations are used for the person re-identification task. Thirdly, learning a similarity metric for person images is a crucial aspect of person re-identification. As the third contribution, we propose a novel listwise loss function taking into account the order in the ranking of gallery images with respect to different probe images. Further, an evaluation gain-based weighting is introduced in the loss function to optimize directly the evaluation measures of person re-identification. At the end, in a large gallery set, many people could have similar clothing. In this case, using only the appearance of single person leads to strong ambiguities. In realistic settings, people often walk in groups rather than alone. As the last contribution, we propose to learn a deep feature representation with displacement invariance for group context and introduce a method to combine the group context and single-person appearance. For all the four contributions of this thesis, we carry out extensive experiments on popular benchmarks and datasets to demonstrate the effectiveness of the proposed systems.
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Aplicação industrial de re-identificação de modelos de MPC em malha fechada. / Industrial application of closed-loop re-identification of MPC models.Pitta, Renato Neves 26 January 2012 (has links)
A identificação de modelos é usualmente a tarefa mais significativa e demorada no trabalho de implementação e manutenção de sistemas de controle que usam Controle Preditivo baseado em Modelos (MPC) tendo em vista a complexidade da tarefa e a importância que o modelo possui para um bom desempenho do controlador. Após a implementação, o controlador tende a permanecer com o modelo original mesmo que mudanças de processo tenham ocorrido levando a uma degradação das ações do controlador. Este trabalho apresenta uma aplicação industrial de re-identificação em malha fechada. A metodologia de excitação da planta utilizada foi apresentada em Sotomayor et al. (2009). Tal técnica permite obter o comportamento das variáveis de processo sem desligar o MPC e sem modificar sua estrutura, aumentando assim, o automatismo e a segurança do procedimento de re-identificação. O sistema re-identificado foi uma coluna debutanizadora de uma refinaria brasileira sendo que os modelos fazem parte do controle preditivo multivariável dessa coluna de destilação. A metodologia foi aplicada com sucesso podendo-se obter os seis novos modelos para atualizar o controlador em questão, o que resultou em uma melhoria de seu desempenho. / Model identification is usually the most significant and time-consuming task of implementing and maintaining control systems based on models (MPC) concerning the complexity of the task and the importance of the model for a good performance of the controller. After being implemented the MPC tends to remain with the original model even after process changes have occurred, leading to a degradation of the controller actions. The present work shows an industrial application of closed-loop re-identification. The plant excitation methodology used here was presented in Sotomayor et al. (2009). Such technique allows for obtaining the behavior of the process variables with the MPC still working and without modifying the MPC structure, increasing automation and safety of the re-identification procedure. The system re-identified was a debutanizer column of a Brazilian refinery being the models part of the multivariable predictive control of this distillation column. The methodology was applied with reasonable success managing to obtain 6 new models to update this MPC, and resulting in improved control performance.
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