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.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. About the Editors - p. xiii 6. Chapter 1: The Group and Crowd Analysis Interdisciplinary Challenge - p. xiv 7. Part 1: Features and Representations - p. 11 8. Chapter 2: Social Interaction in Temporary Gatherings - p. 11 9. Chapter 3: Group Detection and Tracking Using Sociological Features - p. 28 10. Chapter 4: Exploring Multitask and Transfer Learning Algorithms for Head Pose Estimation in Dynamic Multiview Scenarios - p. 66 11. Chapter 5: The Analysis of High Density Crowds in Videos - p. 87 12. Chapter 6: Tracking Millions of Humans in Crowded Spaces - p. 113 13. Chapter 7: Subject-Centric Group Feature for Person Reidentification - p. 135 14. Part 2: Group and Crowd Behavior Modeling - p. 157 15. Chapter 8: From Groups to Leaders and Back - p. 157 16. Chapter 9: Learning to Predict Human Behavior in Crowded Scenes - p. 182 17. Chapter 10: Deep Learning for Scene-Independent Crowd Analysis - p. 207 18. Chapter 11: Physics-Inspired Models for Detecting Abnormal Behaviors in Crowded Scenes - p. 252 19. Chapter 12: Activity Forecasting - p. 272 20. Part 3: Metrics, Benchmarks and Systems - p. 294 21. Chapter 13: Integrating Computer Vision Algorithms and Ontologies for Spectator Crowd Behavior Analysis - p. 294 22. Chapter 14: SALSA: A Multimodal Dataset for the Automated Analysis of Free-Standing Social Interactions - p. 319 23. Chapter 15: Zero-Shot Crowd Behavior Recognition - p. 340 24. Chapter 16: The GRODE Metrics - p. 369 25. Chapter 17: Realtime Pedestrian Tracking and Prediction in Dense Crowds - p. 390 26. Subject Index - p. 417
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.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. About the Editors - p. xiii 6. Chapter 1: The Group and Crowd Analysis Interdisciplinary Challenge - p. xiv 7. Part 1: Features and Representations - p. 11 8. Chapter 2: Social Interaction in Temporary Gatherings - p. 11 9. Chapter 3: Group Detection and Tracking Using Sociological Features - p. 28 10. Chapter 4: Exploring Multitask and Transfer Learning Algorithms for Head Pose Estimation in Dynamic Multiview Scenarios - p. 66 11. Chapter 5: The Analysis of High Density Crowds in Videos - p. 87 12. Chapter 6: Tracking Millions of Humans in Crowded Spaces - p. 113 13. Chapter 7: Subject-Centric Group Feature for Person Reidentification - p. 135 14. Part 2: Group and Crowd Behavior Modeling - p. 157 15. Chapter 8: From Groups to Leaders and Back - p. 157 16. Chapter 9: Learning to Predict Human Behavior in Crowded Scenes - p. 182 17. Chapter 10: Deep Learning for Scene-Independent Crowd Analysis - p. 207 18. Chapter 11: Physics-Inspired Models for Detecting Abnormal Behaviors in Crowded Scenes - p. 252 19. Chapter 12: Activity Forecasting - p. 272 20. Part 3: Metrics, Benchmarks and Systems - p. 294 21. Chapter 13: Integrating Computer Vision Algorithms and Ontologies for Spectator Crowd Behavior Analysis - p. 294 22. Chapter 14: SALSA: A Multimodal Dataset for the Automated Analysis of Free-Standing Social Interactions - p. 319 23. Chapter 15: Zero-Shot Crowd Behavior Recognition - p. 340 24. Chapter 16: The GRODE Metrics - p. 369 25. Chapter 17: Realtime Pedestrian Tracking and Prediction in Dense Crowds - p. 390 26. Subject Index - p. 417
