From front-end
sensors to systems and environmental issues, this practical resource guides you
through the many facets of multimodal surveillance. The book examines thermal,
vibration, video, and audio sensors in a broad context of civilian and military
applications. This cutting-edge volume
provides an in-depth treatment of data fusion algorithms that takes you to the
core of multimodal surveillance, biometrics, and sentient computing. The book
discusses such people and activity topics as tracking people and vehicles and
identifying individuals by their speech.
Systems designers
benefit from discussions on 3-D scene and automatic modeling, high-end sensors
for long-range tracking or high-fidelity, and low-cost sensor solutions.
Developers gain new insight into architectures, frameworks, workbenches,
real-time performance, and systems evaluation. Bringing together the work of
leading international experts, this book is your authoritative reference on
multimodal applications, methodologies, and systems implementation.
Introduction—Multimodal Sensing, Data Fusion, and
Surveillance
Part I: Multimodal Sensors and Sensing Approaches
Multimodal Human Signatures for Personnel Detection.
Human Signatures in Urban Environments Using Multimodal
Unattended Ground Sensors.
Multimodal Image Fusion Systems.
Multimodal Integration for Human Detection and Hearing at a
Distance.
Sensor and Data System, Audio-Assisted Cameras, and Acoustic
Doppler Sensors.
Part II: Multimodal Integration Algorithms
Automatic Audio-Visual Speech Recognition.
Multimodal Tracking for Smart Videoconferencing and Video
Surveillance.
Combination of Ear and Face Images in Appearance-Based
Biometrics.
Multimodal Biometrics of Face and Palm Print.
Multisensory and Multimodal Fusion for Sentient Computing.
Part III: Multimodal Systems
EC-ASSIST: An End-to-End Electronic Chronicling System for
the ASSIST Program.
Systems Issues in Multimodal Surveillance.
A Multimodal Workbench for Automatic Surveillance.
Automatic 3D Modeling of Cities with Multimodal Air and
Ground Sensors.
Multimodal Biometric Systems: Applications and Usage
Scenarios.
A Framework for Multimodal Sensor Stream Analysis,
Transformation, and Querying.
Zhigang Zhu is an associate professor in the Computer Science
Department, City College of New York. An associate editor of Machine Vision and Applications Journal,
he has been involved in organizing such meetings as the IEEE Virtual Reality
Conference, the International Workshop on Digital and Computational Video, and
the IEEE International Workshop on Image and Video Registration. He received his
M.E. and Ph.D. in computer science from Tsinghua University, Beijing.
Thomas Huang is the William L. Everitt Distinguished Professor
of the Department of Electrical and Computer Engineering and of the Coordinated
Science Lab at the University of Illinois at Champaign-Urbana. A frequent
keynote speaker at professional and academic conferences, he has garnered many
professional honors including the Honda Lifetime Achievement Award, IEEE Third
Millennium Medal, and the IEEE Jack S. Kilby Signal Processing Medal. He holds
an M.S. and Sc.D. in electrical engineering from M.I.T.