Alexandria Digital Research Library

Human motivated multicamera video analytics

Author:
De Leo, Carter
Degree Grantor:
University of California, Santa Barbara. Electrical & Computer Engineering
Degree Supervisor:
B. S. Manjunath
Place of Publication:
[Santa Barbara, Calif.]
Publisher:
University of California, Santa Barbara
Creation Date:
2013
Issued Date:
2013
Topics:
Engineering, Electronics and Electrical and Computer Science
Keywords:
Functional MRI
Surveillance
Machine learning
Sensor networks
Video summarization
Computer vision
Genres:
Dissertations, Academic and Online resources
Dissertation:
Ph.D.--University of California, Santa Barbara, 2013
Description:

The continued emergence of inexpensive sensors and storage has made the collection and processing of large quantities of visual data practical, opening up new possibilities in data exploitation and understanding. The volume of data also makes it increasingly difficult to rely solely on humans for review, requiring assistance from automated systems to use large data sources to their full potential. However, while large data has also enabled new algorithmic techniques, computer performance still lags behind that of humans. The work in this thesis addresses both sides of this problem by exploring both how automated systems can make the most of large data and how they can be refined to act more human when doing so. I will discuss video summarization as applied to a network of 11 cameras and show how our system makes the network data more accessible to human operators while also using human feedback to guide its design. A novel approach to object tracking that uses large-scale human annotation to implicitly apply human scene understanding in an automated system will also be discussed. Finally, I will present recent work in using functional magnetic resonance imaging (fMRI) to explore how quantitative human feedback can be directly collected from a subject and applied to debugging traditional computer vision algorithms to bring them closer to human capabilities.

Physical Description:
1 online resource (164 pages)
Format:
Text
Collection(s):
UCSB electronic theses and dissertations
ARK:
ark:/48907/f3tt4nx0
ISBN:
9781303425226
Catalog System Number:
990040770250203776
Rights:
Inc.icon only.dark In Copyright
Copyright Holder:
Carter De Leo
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