Toward Efficient Collaborative Classification For Distributed Video Surveillance
Christopher P. Diehl
Ph.D. Thesis
Department of Electrical and Computer Engineering
Carnegie Mellon University
December 2000
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.
