Group and Crowd Behavior for Computer Vision (Computer Vision and Pattern Recognition) - Hardcover

Über die Autorinnen und Autoren

Full professor at the University of Verona, Italy, and director of the PAVIS (Pattern Analysis and Computer Vision) department at the Istituto Italiano di Tecnologia. He took the Laurea degree in Electronic Engineering in 1989 and a Ph.D. in Electronic Engineering and Computer Science in 1993 at the University of Genova, Italy.
His main research interests include: computer vision and pattern recognition/machine learning, in particular, probabilistic techniques for image and video processing, with applications on video surveillance, biomedical image analysis and bioinformatics.

Associate Professor at the University of Verona, External Collaborator at the Istituto Italiano di Tecnologia, Genova, Italy and Associate Member at the National Research Council. After the Master Degree (with thesis developed partially at the Instituto Superior Tecnico, Lisboa under the supervision of Prof Mario Figueiredo), he took the PhD in Computer Science at the Department of Computer Science of the University of Verona in 2006. During the period 2004-2005 he has been Research Scholar at the University of Southern California, under the guidance of Prof. Ram Nevatia. He has been and is currently scientific collaborator of a number of national and European projects. His research interests include statistical pattern recognition, and especially generative modelling, applied to video surveillance, social signalling and multimedia in general.

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.

Assistant Professor of Computer Science at Stanford University. He earned his Ph.D. in Electrical Engineering from the California Institute of Technology in 2005 and was a Beckman Institute Fellow at the University of Illinois at Urbana-Champaign from 2005–2008. He joined Stanford in 2013 after being Assistant and then Associate Professor (with tenure) of Electrical and Computer Engineering at the University of Michigan, Ann Arbor, from 2008 to 2013. His research interests include computer vision, object recognition and scene understanding, shape representation and reconstruction, human activity recognition and visual psychophysics.

Von der hinteren Coverseite

Group and Crowd Behavior for Computer Vision takes a multidisciplinary perspective to solving the problem of group and crowd analysis and modeling, combining insights from the social sciences withtechnological ideas in computer vision and pattern recognition.Coverage includes:• Detecting a group or a crowd in an image/video, starting from low-level features to the adoption of high-level methods for effectively analyzing and understanding group/crowd behavior• Advanced techniques of segmentation and grouping, tracking and reasoning• Benchmarks that are publicly available in the literature, their peculiarities and limitationsThis book gives researchers in computer vision a comprehensive overview of the state-of-the-art in group and crowd analysis and modeling, including a social (psychological) perspective, making it a valuable resource for those wanting to enter and those already working within this field.Features• Covers the topic of modeling and analysis of groups and crowd in computer vision• Offers a unique cross-disciplinary perspective on group and crowd modeling using anthropological theories translated into computer vision algorithms, showing how groups and crowd behavior analysis can benefit from the findings of social science• Focuses on group and crowd analysis metrics• Shows how real industrial systems can tackle the problem of analyzing groups and crowds