Real-time object classification and novelty detection for collaborative video surveillance

Abstract

To conduct real-time video surveillance using low-cost commercial off-the-shelf hardware, system designers typically define the classifiers prior to the deployment of the system so that the performance of the system can be optimized for a particular mission. This implies the system is restricted to interpreting activity in the environment in terms of the original context specified. Ideally the system should allow the user to provide additional context in an incremental fashion as conditions change. Given the volumes of data produced by the system, it is impractical for the user to periodically review and label a significant fraction of the available data. We explore a strategy for designing a real-time object classification process that aids the user in identifying novel, informative examples for efficient incremental learning.