Carlo Sansone

Vittorio Murino

Enrico Puppo

Marco Cristani

Diego Sona

This book constitutes the refereed proceedings of seven workshops held at the 18th International Conference on Image Analysis and Processing, ICIAP 2015, in Genoa, Italy, in September 2015: International Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2015; International Workshop on Color in Texture and Material Recognition, CTMR 2015; International Workshop on Medical Imaging in Rheumatology: Advanced applications for the analysis of in ammation and damage in the rheumatoid Joint, RHEUMA 2015; International Workshop on Image-Based Smart City Application, ISCA 2015; International Workshop on Multimedia Assisted Dietary Management, MADiMa 2015; International Workshop on Scene Background Modeling and initialization, SBMI 2015; and International Workshop on Image and Video Processing for Quality of Multimedia Experience, QoEM 2015.

ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings

New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops

Shishir Shah

Silvio Savarese

Group and Crowd Behavior for Computer Vision provides a multidisciplinary perspective on how to solve the problem of group and crowd analysis and modeling, combining insights from the social sciences with technological ideas in computer vision and pattern recognition. The book answers many unresolved issues in group and crowd behavior, with Part One providing an introduction to the problems of analyzing groups and crowds that stresses that they should not be considered as completely diverse entities, but as an aggregation of people. Part Two focuses on features and representations with the aim of recognizing the presence of groups and crowds in image and video data. It discusses low level processing methods to individuate when and where a group or crowd is placed in the scene, spanning from the use of people detectors toward more ad-hoc strategies to individuate group and crowd formations. Part Three discusses methods for analyzing the behavior of groups and the crowd once they have been detected, showing how to extract semantic information, predicting/tracking the movement of a group, the formation or disaggregation of a group/crowd and the identification of different kinds of groups/crowds depending on their behavior. The final section focuses on identifying and promoting datasets for group/crowd analysis and modeling, presenting and discussing metrics for evaluating the pros and cons of the various models and methods. This book gives computer vision researcher techniques for segmentation and grouping, tracking and reasoning for solving group and crowd modeling and analysis, as well as more general problems in computer vision and machine learning. Presents the first book to cover the topic of modeling and analysis of groups in computer vision Discusses the topics of group and crowd modeling from a cross-disciplinary perspective, using social science anthropological theories translated into computer vision algorithms Focuses on group and crowd analysis metrics Discusses real industrial systems dealing with the problem of analyzing groups and crowds

Group and Crowd Behavior for Computer Vision

Shaogang Gong

Shuicheng Yan

Chen Change Loy

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.

Person Re-Identification

Marco Cristani

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