Ph.D. Thesis Announcement

[Diehl, Chris P.]

Dear Connectionists,

The following Ph.D. thesis is now available at http://www.cpdiehl.org.

Toward Efficient Collaborative Classification for Distributed Video
Surveillance

Christopher P. Diehl
Ph.D. Thesis
Department of Electrical and Computer Engineering
Carnegie Mellon University

Abstract

In this thesis, we propose a general strategy for automated video
surveillance that relies on collaboration between the surveillance
system and the user.  Such collaboration enables the user to help the
system incrementally acquire the necessary context for truly robust
surveillance. The success of this strategy is dependent on the ability
of the system to identify novel instances of known or unknown classes
that it does not understand. This, in turn, allows the user to focus
only on the observations with the highest uncertainty that require
interpretation.

Designing a real-time classification process that supports novelty
detection is nontrivial. The real-time constraint dictates
computational simplicity, whereas novelty detection requires a high
dimensional feature space to aid in discriminating between the known
and unknown classes. The majority of this work focuses on the problem
of simultaneously satisfying these conflicting constraints.

We consider these issues in the context of a relevant surveillance
task and evaluate the performance of the resulting classification
process in the CMU Cyberscout distributed video surveillance system.

Dr. Chris Diehl
System and Information Sciences Group
Research and Technology Development Center
Applied Physics Laboratory
Johns Hopkins University

443-778-3457 (Office)
443-778-6904 (Fax)
http://www.cpdiehl.org