Group and Crowd Behavior for Computer Vision

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 visionDiscusses the topics of group and crowd modeling from a cross-disciplinary perspective, using social science anthropological theories translated into computer vision algorithmsFocuses on group and crowd analysis metricsDiscusses real industrial systems dealing with the problem of analyzing groups and crowds

1. The Group and Crowd Analysis Interdisciplinary Challenge

Part 1: Features and Representations 2. Social Interaction in Temporary Gatherings 3. Group Detection and Tracking Using Sociological Features 4. Exploring Multitask and Transfer Learning Algorithms for Head Pose Estimation in Dynamic Multiview Scenarios 5. The Analysis of High Density Crowds in Videos 6. Tracking Millions of Humans in Crowded Spaces 7. Subject-Centric Group Feature for Person Reidentification

Part 2: Group and Crowd Behavior Modeling 8. From Groups to Leaders and Back 9. Learning to Predict Human Behavior in Crowded Scenes 10. Deep Learning for Scene-Independent Crowd Analysis 11. Physics-Inspired Models for Detecting Abnormal Behaviors in Crowded Scenes 12. Activity Forecasting

Part 3: Metrics, Benchmarks and Systems 13. Integrating Computer Vision Algorithms and Ontologies for Spectator Crowd Behavior Analysis 14. SALSA: A Multimodal Dataset for the Automated Analysis of Free-Standing Social Interactions  15. Zero-Shot Crowd Behavior Recognition 16. The GRODE Metrics 17. Realtime Pedestrian Tracking and Prediction in Dense Crowds