
Group and Crowd Behavior for Computer Vision
Description
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
- 17.5. Realtime Pedestrian Path Prediction
- 17.6. Implementation and Results
- 17.7. Conclusion
- References
Product details
- No. of pages: 438
- Language: English
- Copyright: © Academic Press 2017
- Published: April 10, 2017
- Imprint: Academic Press
- eBook ISBN: 9780128092804
- Hardcover ISBN: 9780128092767
About the Authors
Vittorio Murino
Affiliations and Expertise
Marco Cristani
Affiliations and Expertise
Shishir Shah
Studied Mechanical Engineering as an undergraduate at The University of Texas at Austin, where he received his B.S. degree in 1994. He received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from The University of Texas at Austin for his thesis on Vision-based Mobile Robot Navigation and Probabilistic Feature Integration for Object Recognition, respectively.
His current research focuses on human behavior modeling and analysis, scene understanding, video analytics, biometrics, and microscopy image analysis. His long-term interests are centered on the broader area of knowledge driven intelligent systems capable of seamless incorporation of semantic information through statistical decision priors and data driven feedback, with the intent of developing ‘visual decision’ capabilities that would include cognitive functions for reasoning and learning.
Affiliations and Expertise
Silvio Savarese
Affiliations and Expertise
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