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.
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.
14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings
Author: Bastian Leibe
The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physicsbased vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action activity and tracking; 3D; and 9 poster sessions.
25th International Conference, MMM 2019, Thessaloniki, Greece, January 8–11, 2019, Proceedings
Author: Ioannis Kompatsiaris
The two-volume set LNCS 11295 and 11296 constitutes the thoroughly refereed proceedings of the 25th International Conference on MultiMedia Modeling, MMM 2019, held in Thessaloniki, Greece, in January 2019. Of the 172 submitted full papers, 49 were selected for oral presentation and 47 for poster presentation; in addition, 6 demonstration papers, 5 industry papers, 6 workshop papers, and 6 papers for the Video Browser Showdown 2019 were accepted. All papers presented were carefully reviewed and selected from 204 submissions.
15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings
Author: Vittorio Ferrari
The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
Highlights: An equidistance constrained metric learning algorithm for person re-identification is proposed. In our method, points of the same class are collapsed into a single point, while points of different classes are mapped to different vertices of a regular simplex. Our method aims to guarantee the best separability of the training data, meanwhile, promote the generalization ability of the learned metric. Abstract: Person re-identification (re-id), aiming to search a specific person among a non-overlapping camera network, has attracted plenty of interest in recent years. This task is highly challenging, especially when there exists only single image per person in the database. In this paper, we present an algorithm for learning a Mahalanobis distance for person re-identification. Our method has two distinctive features: (1) to obtain the best separability of the training data, we first minimize the intra-class distances to the most extent by forcing intra-class distances to be zero, and (2) to promote the generalization ability of the learned metric, we then maximize the minimum margin between different classes. Inspired by the simple geometric intuition that a regular simplex maximizes its minimum side length, provided the sum of all side length is fixed, our method, called EquiDistance constrained Metric Learning (EquiDML), applies least-square regression technique to map images of the same person to the same vertex of a regular simplex, and images of different persons to different vertices of a regular simplex. Consequently, under the learned metric, images of the same class are collapsed to a single point, while images of different classes are transformed to be equidistant. This simple motivation is further formulated as a convex optimization problem, solved by the projected gradient descent method and proved to be very effective in person re-identification task. Although it is fairly simple, our method outperforms the state-of-the-art methods on CUHK01, CUHK03, Market1501 and DukeMTMC-reID datasets, and achieves very competitive performance on the widely used VIPeR dataset.
ACCV 2010 International Workshops. Queenstown, New Zealand, November 8-9, 2010. Revised Selected Papers
Author: Reinhard Koch
Publisher: Springer Science & Business Media
The two-volume set LNCS 6468-6469 contains the carefully selected and reviewed papers presented at the eight workshops that were held in conjunction with the 10th Asian Conference on Computer Vision, in Queenstown, New Zealand, in November 2010. From a total of 167 submissions to all workshops, 89 papers were selected for publication. The contributions are grouped together according to the main workshops topics, which were: computational photography and aesthetics; computer vision in vehicle technology: from Earth to Mars; electronic cultural heritage; subspace based methods; video event categorization, tagging and retrieval; visual surveillance; application of computer vision for mixed and augmented reality.
First International Workshop, SIMBAD 2011, Venice, Italy, September 28-30, 2011, Proceedings
Author: Marcello Pelillo
Publisher: Springer Science & Business Media
This book constitutes the proceedings of the First International Workshop on Similarity Based Pattern Recognition, SIMBAD 2011, held in Venice, Italy, in September 2011. The 16 full papers and 7 poster papers presented were carefully reviewed and selected from 35 submissions. The contributions are organized in topical sections on dissimilarity characterization and analysis; generative models of similarity data; graph-based and relational models; clustering and dissimilarity data; applications; spectral methods and embedding.
15th International Conference, Salamanca, Spain, September 10-12, 2014, Proceedings
Author: Emilio Corchado
This book constitutes the refereed proceedings of the 15th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2014, held in Salamanca, Spain, in September 2014. The 60 revised full papers presented were carefully reviewed and selected from about 120 submissions. These papers provided a valuable collection of recent research outcomes in data engineering and automated learning, from methodologies, frameworks, and techniques to applications. In addition the conference provided a good sample of current topics from methodologies, frameworks, and techniques to applications and case studies. The techniques include computational intelligence, big data analytics, social media techniques, multi-objective optimization, regression, classification, clustering, biological data processing, text processing, and image/video analysis.
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.