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GROUP AND CROWD BEHAVIOR FOR COMPUTER VISION
Título:
GROUP AND CROWD BEHAVIOR FOR COMPUTER VISION
Subtítulo:
Autor:
MURINO, V
Editorial:
ACADEMIC PRESS
Año de edición:
2017
Materia
VISION POR ORDENADOR
ISBN:
978-0-12-809276-7
Páginas:
438
134,50 €

 

Sinopsis

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.



Key Features
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
Readership
Computer scientists and electronic researchers in computer vision and pattern recognition; graduate students in these fields

Table of Contents
Chapter 1: The Group and Crowd Analysis Interdisciplinary Challenge

Abstract
1.1. The Study of Groups and Crowds
1.2. Scope of the Book
1.3. Summary of Important Points
References
Part 1: Features and Representations

Chapter 2: Social Interaction in Temporary Gatherings

Abstract
2.1. Introduction: Group and Crowd Behavior in Context
2.2. Social Interaction: A Typology and Some Definitions
2.3. Temporary Gatherings: A Taxonomy and Some Examples
2.4. Conclusion: Microsociology Applied to Computer Vision
2.5. Further Reading
References
Chapter 3: Group Detection and Tracking Using Sociological Features

Abstract
3.1. Introduction
3.2. State-of-the-Art
3.3. Sociological Features
3.4. Detection Models
3.5. Group Tracking
3.6. Experiments
3.7. Discussion
3.8. Conclusions
References
Chapter 4: Exploring Multitask and Transfer Learning Algorithms for Head Pose Estimation in Dynamic Multiview Scenarios

Abstract
4.1. Introduction
4.2. Related Work
4.3. TL and MTL for Multiview Head Pose Estimation
4.4. Conclusions
References
Chapter 5: The Analysis of High Density Crowds in Videos

Abstract
5.1. Introduction
5.2. Literature Review
5.3. Data-Driven Crowd Analysis in Videos
5.4. Density-Aware Person Detection and Tracking in Crowds
5.5. CrowdNet: Learning a Representation for High Density Crowds in Videos
5.6. Conclusions and Directions for Future Research
References
Chapter 6: Tracking Millions of Humans in Crowded Spaces

Abstract
6.1. Introduction
6.2. Related Work
6.3. System Overview
6.4. Human Detection in 3D
6.5. Tracklet Generation
6.6. Tracklet Association
6.7. Experiments
6.8. Conclusions
References
Chapter 7: Subject-Centric Group Feature for Person Reidentification

Abstract
Acknowledgments
7.1. Introduction
7.2. Related Works
7.3. Methodology
7.4. Results
7.5. Conclusion
References
Part 2: Group and Crowd Behavior Modeling

Chapter 8: From Groups to Leaders and Back

Abstract
8.1. Introduction
8.2. Modeling and Observing Groups and Their Leaders in Literature
8.3. Technical Preliminaries and Structured Output Prediction
8.4. The Tools of the Trade in Social and Structured Crowd Analysis
8.5. Results on Visual Localization of Groups and Leaders
8.6. The Predictive Power of Leaders in Social Groups
8.7. Conclusion
References
Chapter 9: Learning to Predict Human Behavior in Crowded Scenes

Abstract
9.1. Introduction
9.2. Related Work
9.3. Forecasting with Social Forces Model
9.4. Forecasting with Recurrent Neural Network
9.5. Experiments
9.6. Conclusions
References
Chapter 10: Deep Learning for Scene-Independent Crowd Analysis

Abstract
10.1. Introduction
10.2. Large Scale Crowd Datasets
10.3. Crowd Counting and Density Estimation
10.4. Attributes for Crowded Scene Understanding
10.5. Conclusion
References
Chapter 11: Physics-Inspired Models for Detecting Abnormal Behaviors in Crowded Scenes

Abstract
11.1. Introduction
11.2. Crowd Anomaly Detection: A General Review
11.3. Physics-Inspired Crowd Models
11.4. Violence Detection
11.5. Experimental Results
11.6. Conclusions
References
Chapter 12: Activity Forecasting

Abstract
12.1. Introduction
12.2. Overview
12.3. Activity Forecasting as Optimal Control
12.4. Single Agent Trajectory Forecasting in Static Environment
12.5. Multiagent Trajectory Forecasting
12.6. Dual-Agent Interaction Forecasting
12.7. Final Remarks
References
Part 3: Metrics, Benchmarks and Systems

Chapter 13: Integrating Computer Vision Algorithms and Ontologies for Spectator Crowd Behavior Analysis

Abstract
Acknowledgments
13.1. Introduction
13.2. Computer Vision and Ontology
13.3. An Extension of the dolce Ontology for Spectator Crowd
13.4. Reasoning on the Temporal Alignment of Stands and Playground
13.5. Concluding Remarks
References
Chapter 14: SALSA: A Multimodal Dataset for the Automated Analysis of Free-Standing Social Interactions

Abstract
14.1. Introduction
14.2. Literature Review
14.3. Spotting the Research Gap
14.4. The SALSA Dataset
14.5. Experiments on SALSA
14.6. Conclusions and Future Work
References
Chapter 15: Zero-Shot Crowd Behavior Recognition

Abstract
15.1. Introduction
15.2. Related Work
15.3. Methodology
15.4. Experiments
15.5. Further Analysis
15.6. Conclusions
References
Chapter 16: The GRODE Metrics

Abstract
16.1. Introduction
16.2. Metrics in the Literature
16.3. The GRODE Metrics
16.4. Experiments
16.5. Conclusions
References
Chapter 17: Realtime Pedestrian Tracking and Prediction in Dense Crowds

Abstract
Acknowledgments
17.1. Introduction
17.2. Related Work
17.3. Pedestrian State
17.4. Mixture Motion Model