CiNii 図書 - 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.

1. The Group and Crowd Analysis Interdisciplinary Challenge Part 1: Features and Representations 2. Social Interaction in Temporary Gatherings3. Group Detection and Tracking Using Sociological Features4. Exploring Multitask and Transfer Learning Algorithms for Head Pose Estimation in Dynamic Multiview Scenarios5. The Analysis of High Density Crowds in Videos6. Tracking Millions of Humans in Crowded Spaces7. Subject-Centric Group Feature for Person Reidentification Part 2: Group and Crowd Behavior Modeling8. From Groups to Leaders and Back9. Learning to Predict Human Behavior in Crowded Scenes10. Deep Learning for Scene-Independent Crowd Analysis11. Physics-Inspired Models for Detecting Abnormal Behaviors in Crowded Scenes12. Activity Forecasting Part 3: Metrics, Benchmarks and Systems13. 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 Recognition16. The GRODE Metrics17. Realtime Pedestrian Tracking and Prediction in Dense Crowds

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Group and crowd behavior for computer vision

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