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Human Re Identification Through A Video Camera Network
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Book Synopsis Human Re-identification Through a Video Camera Network by : Slawomir Bąk
Download or read book Human Re-identification Through a Video Camera Network written by Slawomir Bąk and published by . This book was released on 2012 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis targets the appearance-based re-identification of humans in images and videos. Human re-identification is defined as a requirement to determine whether a given individual has already appeared over a network of cameras. This problem is particularly hard by significant appearance changes across different camera views, where variations in viewing angle, illumination and object pose, make the problem challenging. We focus on developing robust appearance models that are able to match human appearances registered in disjoint camera views. As encoding of image regions is fundamental for appearance matching, we study different kinds of image descriptors. These different descriptors imply different strategies for appearance matching, bringing different models for the human appearance representation. By applying machine learning techniques, we generate descriptive and discriminative models, which enhance distinctive characteristics of extracted features, improving re-identification accuracy. This thesis makes the following contributions. We propose six techniques for human re-identification. The first two belong to single-shot approaches, in which a single image is sufficient to extract a robust signature. These approaches divide the human body into the predefined body parts and then extract image features. This allows to establish the corresponding body parts, while comparing signatures. The remaining four methods address the re-identification problem using signatures computed from multiple images (multiple-shot case). We propose two techniques which learn online the human appearance model using a boosting scheme. The boosting approaches improve recognition accuracy at the expense of time consumption. The last two approaches either assume the predefined model, or learn offline a model, to meet time requirements. We find that covariance feature is in general the best descriptor for matching appearances across disjoint camera views. As a distance operator of this descriptor is computationally intensive, we also propose a new GPU-based implementation which significantly speeds up computations. Our experiments suggest that mean Riemannian covariance computed from multiple images improves state of the art performance of human re-identification techniques. Finally, we extract two new image sets of individuals for evaluating the multiple-shot scenario.
Book Synopsis Human Re-Identification by : Ziyan Wu
Download or read book Human Re-Identification written by Ziyan Wu and published by Springer. This book was released on 2016-09-08 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers aspects of human re-identification problems related to computer vision and machine learning. Working from a practical perspective, it introduces novel algorithms and designs for human re-identification that bridge the gap between research and reality. The primary focus is on building a robust, reliable, distributed and scalable smart surveillance system that can be deployed in real-world scenarios. This book also includes detailed discussions on pedestrian candidates detection, discriminative feature extraction and selection, dimension reduction, distance/metric learning, and decision/ranking enhancement.This book is intended for professionals and researchers working in computer vision and machine learning. Advanced-level students of computer science will also find the content valuable.
Book Synopsis Person Re-Identification with Limited Supervision by : Rameswar Panda
Download or read book Person Re-Identification with Limited Supervision written by Rameswar Panda and published by Springer Nature. This book was released on 2022-06-01 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: Person re-identification is the problem of associating observations of targets in different non-overlapping cameras. Most of the existing learning-based methods have resulted in improved performance on standard re-identification benchmarks, but at the cost of time-consuming and tediously labeled data. Motivated by this, learning person re-identification models with limited to no supervision has drawn a great deal of attention in recent years. In this book, we provide an overview of some of the literature in person re-identification, and then move on to focus on some specific problems in the context of person re-identification with limited supervision in multi-camera environments. We expect this to lead to interesting problems for researchers to consider in the future, beyond the conventional fully supervised setup that has been the framework for a lot of work in person re-identification. Chapter 1 starts with an overview of the problems in person re-identification and the major research directions. We provide an overview of the prior works that align most closely with the limited supervision theme of this book. Chapter 2 demonstrates how global camera network constraints in the form of consistency can be utilized for improving the accuracy of camera pair-wise person re-identification models and also selecting a minimal subset of image pairs for labeling without compromising accuracy. Chapter 3 presents two methods that hold the potential for developing highly scalable systems for video person re-identification with limited supervision. In the one-shot setting where only one tracklet per identity is labeled, the objective is to utilize this small labeled set along with a larger unlabeled set of tracklets to obtain a re-identification model. Another setting is completely unsupervised without requiring any identity labels. The temporal consistency in the videos allows us to infer about matching objects across the cameras with higher confidence, even with limited to no supervision. Chapter 4 investigates person re-identification in dynamic camera networks. Specifically, we consider a novel problem that has received very little attention in the community but is critically important for many applications where a new camera is added to an existing group observing a set of targets. We propose two possible solutions for on-boarding new camera(s) dynamically to an existing network using transfer learning with limited additional supervision. Finally, Chapter 5 concludes the book by highlighting the major directions for future research.
Book Synopsis Open-world Person Re-identification by : Ye Mang
Download or read book Open-world Person Re-identification written by Ye Mang and published by . This book was released on 2019 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increasing demand of intelligent video surveillance systems, person re-identification (re-ID) plays an important role in intelligent video analysis, which aims at matching person images across non-overlapping camera views. It has gained increasing attention in computer vision community. With the advanced deep neural networks, existing methods have achieved promising performance on the widely-used re-ID benchmarks, even outperform the human-level rank-1 matching accuracy. However, most of the research efforts are conducted on the closed-world settings, with large-scale well annotated training data and all the person images are from the same visible modality. As a prerequisite in practical video surveillance application, there is still a large gap between the closed-world research-oriented setting and the practical open-world settings. In this thesis, we try to narrow the gap by studying three important issues in open-world person re-identification, including 1) unsupervised learning with large-scale unlabelled training data; 2) learning robust re-ID model with label corrupted training data and 3) cross-modality visible-thermal person re-identification with multi-modality data. For unsupervised learning with unlabelled training data, we mainly focus on video-based person re-identification, since the video data is usually easily obtained by tracking algorithms and the video sequence provides rich weakly labelled samples by assuming the image frames within the tracked sequence belonging to the same person identity. Following the cross-camera label estimation approach, we formulate the cross-camera label estimation as a one-to-one graph matching problem, and then propose a novel dynamic graph matching framework to estimate cross-camera labels. However, in a practical wild scenario, the unlabelled training data usually cannot satisfy the one-to-one matching constraint, which would result in a large proportion of false positives. To address this issue, we further propose a novel robust anchor embedding method for unsupervised video re-ID. In the proposed method, some anchor sequences are firstly selected to initialize the CNN feature representation. Then a robust anchor embedding method is proposed to measure the relationship between the unlabelled sequences and anchor sequences, which considers both the scalability and efficiency. After that, a top-$k$ counts label prediction strategy is proposed to predict the labels of unlabelled sequences. With the newly estimated sequences, the CNN representation could be further updated. For robust re-ID model learning with label corrupted training data, we propose a two-stage learning method to handle the label noise. Rather than simply filtering the falsely annotated samples, we propose a joint learning method by simultaneously refining the falsely annotated labels and optimizing the neural networks. To address the limited training samples for each identity, we further propose a novel hard-aware instance re-weighting strategy to fine-tune the learned model, which assigns larger weights to hard samples with correct labels. For cross-modality visible-thermal person re-identification, it addresses an important issue in night-time surveillance applications by matching person images across different modalities. We propose a dual-path network to learn the cross-modality feature representations, which learns the multi-modality sharable feature representations by simultaneously considering the modality discrepancy and commonness. To guide the feature representation learning process, we propose a dual-constrained top-ranking loss, which contains both cross-modality and intra-modality top-ranking constraints to reduce the large cross-modality and intra-modality variations. Besides the open-world person re-identification, we have also studied the unsupervised embedding learning problem for general image classification and retrieval. Motivated by supervised embedding learning, we propose a data augmentation invariant and instance spread-out feature. To learn the feature embedding, we propose a instance feature-based softmax embedding, which optimizes the embedding directly on top of the real-time instance features. It achieves much faster learning speed and better accuracy than existing methods. In short, the major contributions of this thesis are summarized as follows. l A dynamic graph matching framework is proposed to estimate cross-camera labels for unsupervised video-based person re-identification. l A robust anchor embedding method with top-$k$ counts label prediction is proposed to efficiently estimate the cross-camera labels for unsupervised video-based person re-identification under wild settings. l A two-stage PurifyNet is introduced to handle the label noise problem in person re-identification, which jointly refines the falsely annotated labels and mines hard samples with correct labels. l A dual-constrained top-ranking loss with a dual-path network is proposed for cross-modality visible-thermal person re-identification, which simultaneously addresses the cross-modality and intra-modality variations. l A data augmentation invariant and instance spread-out feature is proposed for unsupervised embedding learning, which directly optimizes the learned embedding on top of real-time instance features with softmax function
Book Synopsis Computer Vision -- ECCV 2014 by : David Fleet
Download or read book Computer Vision -- ECCV 2014 written by David Fleet and published by Springer. This book was released on 2014-09-22 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.
Book Synopsis Person Re-identification with Limited Labeled Training Data by : Jiawei Li
Download or read book Person Re-identification with Limited Labeled Training Data written by Jiawei Li and published by . This book was released on 2018 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the growing installation of surveillance video cameras in both private and public areas, it is an immediate requirement to develop intelligent video analysis system for the large-scale camera network. As a prerequisite step of person tracking and person retrieval in intelligent video analysis, person re-identification, which targets in matching person images across camera views is an important topic in computer vision community and has been received increasing attention in the recent years. In the supervised learning methods, the person re-identification task is formulated as a classification problem to extract matched person images/videos (positives) from unmatched person images/videos (negatives). Although the state-of-the-art supervised classification models could achieve encouraging re-identification performance, the assumption that label information is available for all the cameras, is impractical in large-scale camera network. That is because collecting the label information of every training subject from every camera in the large-scale network can be extremely time-consuming and expensive. While the unsupervised learning methods are flexible, their performance is typically weaker than the supervised ones. Though sufficient labels of the training subjects are not available from all the camera views, it is still reasonable to collect sufficient labels from a pair of camera views in the camera network or a few labeled data from each camera pair. Along this direction, we address two scenarios of person re-identification in large-scale camera network in this thesis, i.e. unsupervised domain adaptation and semi-supervised learning and proposed three methods to learn discriminative model using all available label information and domain knowledge in person re-identification. In the unsupervised domain adaptation scenario, we consider data with sufficient labels as the source domain, while data from the camera pair missing label information as the target domain. A novel domain adaptive approach is proposed to estimate the target label information and incorporate the labeled data from source domain with the estimated target label information for discriminative learning. Since the discriminative constraint of Support Vector Machines (SVM) can be relaxed into a necessary condition, which only relies on the mean of positive pairs (positive mean), a suboptimal classification model learning without target positive data can be those using target positive mean. A reliable positive mean estimation is given by using both the labeled data from the source domain and potential positive data selected from the unlabeled data in the target domain. An Adaptive Ranking Support Vector Machines (AdaRSVM) method is also proposed to improve the discriminability of the suboptimal mean based SVM model using source labeled data. Experimental results demonstrate the effectiveness of the proposed method. Different from the AdaRSVM method that using source labeled data, we can also improve the above mean based method by adapting it onto target unlabeled data. In more general situation, we improve a pre-learned classifier by adapting it onto target unlabeled data, where the pre-learned classifier can be domain adaptive or learned from only source labeled data. Since it is difficult to estimate positives from the imbalanced target unlabeled data, we propose to alternatively estimate positive neighbors which refer to data close to any true target positive. An optimization problem for positive neighbor estimation from unlabeled data is derived and solved by aligning the cross-person score distributions together with optimizing for multiple graphs based label propagation. To utilize the positive neighbors to learn discriminative classification model, a reliable multiple region metric learning method is proposed to learn a target adaptive metric using regularized affine hulls of positive neighbors as positive regions. Experimental results demonstrate the effectiveness of the proposed method. In the semi-supervised learning scenario, we propose a discriminative feature learning using all available information from the surveillance videos. To enrich the labeled data from target camera pair, image sequences (videos) of the tagged persons are collected from the surveillance videos by human tracking. To extract the discriminative and adaptable video feature representation, we propose to model the intra-view variations by a video variation dictionary and a video level adaptable feature by multiple sources domain adaptation and an adaptability-discriminability fusion. First, a novel video variation dictionary learning is proposed to model the large intra-view variations and solved as a constrained sparse dictionary learning problem. Second, a frame level adaptable feature is generated by multiple sources domain adaptation using the variation modeling. By mining the discriminative information of the frames from the reconstruction error of the variation dictionary, an adaptability-discriminability (AD) fusion is proposed to generate the video level adaptable feature. Experimental results demonstrate the effectiveness of the proposed method.
Book Synopsis Person Re-Identification by : Shaogang Gong
Download or read book Person Re-Identification written by Shaogang Gong and published by Springer Science & Business Media. This book was released on 2014-01-03 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Features: introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms and examines the benefits of semantic attributes; describes how to segregate meaningful body parts from background clutter; examines the use of 3D depth images and contextual constraints derived from the visual appearance of a group; reviews approaches to feature transfer function and distance metric learning and discusses potential solutions to issues of data scalability and identity inference; investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference and describes techniques for improving post-rank search efficiency; explores the design rationale and implementation considerations of building a practical re-identification system.
Book Synopsis Human Identification Based on Gait by : Mark S. Nixon
Download or read book Human Identification Based on Gait written by Mark S. Nixon and published by Springer Science & Business Media. This book was released on 2010-05-26 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human Identification Based on Gait is the first book to address gait as a biometric. Biometrics is now in a unique position where it affects most people's lives. This is especially true of "gait", which is one of the most recent biometrics. Recognizing people by the way they walk and run implies analyzing movement which, in turn, implies analyzing sequences of images, thus requiring memory and computational performance that became available only recently. Human Identification Based on Gait introduces developments from distinguished researchers within this relatively new area of biometrics. This book clearly establishes how human gait is biometric. Human Identification Based on Gait is structured to meet the needs of professionals in industry, as well as advanced-level students in computer science.
Book Synopsis Computational Intelligence and Its Applications by : Abdelmalek Amine
Download or read book Computational Intelligence and Its Applications written by Abdelmalek Amine and published by Springer. This book was released on 2018-04-26 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th IFIP TC 5 International Conference on Computational Intelligence and Its Applications, CIIA 2018, held in Oran, Algeria, in May 2018. The 56 full papers presented were carefully reviewed and selected from 202 submissions. They are organized in the following topical sections: data mining and information retrieval; evolutionary computation; machine learning; optimization; planning and scheduling; wireless communication and mobile computing; Internet of Things (IoT) and decision support systems; pattern recognition and image processing; and semantic web services.
Book Synopsis Visual Analysis of Behaviour by : Shaogang Gong
Download or read book Visual Analysis of Behaviour written by Shaogang Gong and published by Springer Science & Business Media. This book was released on 2011-05-26 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a comprehensive treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives. Topics: covers learning-group activity models, unsupervised behaviour profiling, hierarchical behaviour discovery, learning behavioural context, modelling rare behaviours, and “man-in-the-loop” active learning; examines multi-camera behaviour correlation, person re-identification, and “connecting-the-dots” for abnormal behaviour detection; discusses Bayesian information criterion, Bayesian networks, “bag-of-words” representation, canonical correlation analysis, dynamic Bayesian networks, Gaussian mixtures, and Gibbs sampling; investigates hidden conditional random fields, hidden Markov models, human silhouette shapes, latent Dirichlet allocation, local binary patterns, locality preserving projection, and Markov processes; explores probabilistic graphical models, probabilistic topic models, space-time interest points, spectral clustering, and support vector machines.
Book Synopsis People Re-identification in a Camera Network by : Ghadeer Shaaya
Download or read book People Re-identification in a Camera Network written by Ghadeer Shaaya and published by . This book was released on 2012 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this research, we present an appearance based method for people re-identification. It consists in the extraction of two types of features related to human appearance, color histograms and SIFT features. Images are captured from surveillance videos. For every image, the two types of features are combined to create a two dimensional signature that represents the contained individual. The goal is to make this signature as distinctive as possible. The signatures are arranged into pairs to form positive examples (two images of the same individual) and negative examples (two images of two different individuals). Pairs are fed to a machine learning algorithm. The algorithm is trained to find the most discriminative model. AdaBoost is what we used to perform this task. The algorithm presented in this thesis has been tested on several datasets (ViPER, CAVIAR, 3DPes).
Book Synopsis Intelligent Computing Methodologies by : De-Shuang Huang
Download or read book Intelligent Computing Methodologies written by De-Shuang Huang and published by Springer Nature. This book was released on 2020-10-15 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNCS 12463 and LNCS 12464 constitutes - in conjunction with the volume LNAI 12465 - the refereed proceedings of the 16th International Conference on Intelligent Computing, ICIC 2020, held in Bari, Italy, in October 2020. The 162 full papers of the three proceedings volumes were carefully reviewed and selected from 457 submissions. The ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. The theme for this conference is “Advanced Intelligent Computing Methodologies and Applications.” Papers related to this theme are especially solicited, addressing theories, methodologies, and applications in science and technology.
Book Synopsis Image Analysis and Recognition by : Aurélio Campilho
Download or read book Image Analysis and Recognition written by Aurélio Campilho and published by Springer. This book was released on 2018-06-19 with total page 951 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the 15th International Conference on Image Analysis and Recognition, ICIAR 2018, held in Póvoa de Varzim, Portugal, in June 2018. The 91 full papers presented together with 15 short papers were carefully reviewed and selected from 179 submissions. The papers are organized in the following topical sections: Enhancement, Restoration and Reconstruction, Image Segmentation, Detection, Classication and Recognition, Indexing and Retrieval, Computer Vision, Activity Recognition, Traffic and Surveillance, Applications, Biomedical Image Analysis, Diagnosis and Screening of Ophthalmic Diseases, and Challenge on Breast Cancer Histology Images.
Book Synopsis Human Re-identification in Real-world Surveillance Camera Networks by : Yang Li
Download or read book Human Re-identification in Real-world Surveillance Camera Networks written by Yang Li and published by . This book was released on 2015 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Pattern Recognition and Computer Vision by : Jian-Huang Lai
Download or read book Pattern Recognition and Computer Vision written by Jian-Huang Lai and published by Springer. This book was released on 2018-11-01 with total page 589 pages. Available in PDF, EPUB and Kindle. Book excerpt: The four-volume set LNCS 11056, 110257, 11258, and 11073 constitutes the refereed proceedings of the First Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018, held in Guangzhou, China, in November 2018. The 179 revised full papers presented were carefully reviewed and selected from 399 submissions. The papers have been organized in the following topical sections: Part I: Biometrics, Computer Vision Application. Part II: Deep Learning. Part III: Document Analysis, Face Recognition and Analysis, Feature Extraction and Selection, Machine Learning. Part IV: Object Detection and Tracking, Performance Evaluation and Database, Remote Sensing.
Book Synopsis Pervasive Computing and the Networked World by : Qiaohong Zu
Download or read book Pervasive Computing and the Networked World written by Qiaohong Zu and published by Springer. This book was released on 2014-07-01 with total page 848 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the Joint International Conference on Pervasive Computing and Web Society, ICPCA/SWS 2013, held in Vina de Mar, Chile, in December 2013. The 56 revised full papers presented together with 29 poster papers were carefully reviewed and selected from 156 submissions. The papers are organized in topical sections on infrastructure and devices; service and solution; data and knowledge; as well as community.
Book Synopsis Wide Area Surveillance by : Vijayan K. Asari
Download or read book Wide Area Surveillance written by Vijayan K. Asari and published by Springer Science & Business Media. This book was released on 2013-11-19 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book describes a system for visual surveillance using intelligent cameras. The camera uses robust techniques for detecting and tracking moving objects. The real time capture of the objects is then stored in the database. The tracking data stored in the database is analysed to study the camera view, detect and track objects, and study object behavior. These set of models provide a robust framework for coordinating the tracking of objects between overlapping and non-overlapping cameras, and recording the activity of objects detected by the system.
