Group and Crowd Behavior for Computer Vision

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
• 17.5. Realtime Pedestrian Path Prediction
• 17.6. Implementation and Results
• 17.7. Conclusion
• References