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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… Read more
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Immediately download your ebook while waiting for your print delivery. No promo code needed.
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.
Computer scientists and electronic researchers in computer vision and pattern recognition; graduate students in these fields
Chapter 1: The Group and Crowd Analysis Interdisciplinary Challenge
Part 1: Features and Representations
Chapter 2: Social Interaction in Temporary Gatherings
Chapter 3: Group Detection and Tracking Using Sociological Features
Chapter 4: Exploring Multitask and Transfer Learning Algorithms for Head Pose Estimation in Dynamic Multiview Scenarios
Chapter 5: The Analysis of High Density Crowds in Videos
Chapter 6: Tracking Millions of Humans in Crowded Spaces
Chapter 7: Subject-Centric Group Feature for Person Reidentification
Part 2: Group and Crowd Behavior Modeling
Chapter 8: From Groups to Leaders and Back
Chapter 9: Learning to Predict Human Behavior in Crowded Scenes
Chapter 10: Deep Learning for Scene-Independent Crowd Analysis
Chapter 11: Physics-Inspired Models for Detecting Abnormal Behaviors in Crowded Scenes
Chapter 12: Activity Forecasting
Part 3: Metrics, Benchmarks and Systems
Chapter 13: Integrating Computer Vision Algorithms and Ontologies for Spectator Crowd Behavior Analysis
Chapter 14: SALSA: A Multimodal Dataset for the Automated Analysis of Free-Standing Social Interactions
Chapter 15: Zero-Shot Crowd Behavior Recognition
Chapter 16: The GRODE Metrics
Chapter 17: Realtime Pedestrian Tracking and Prediction in Dense Crowds
VM
MC
SS
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.
SS