EAGER: Video Analytics in Large Heterogeneous Repositories

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The planned research will take video analysis indexing and retrieval in a new and promising direction. The research is driven by the need for intelligence analysts to be able to express video queries more efficiently than traditional relevance feedback and to be able to "provide more expressive queries that include "nouns" and "verbs" as they would with human language. While still constrained, the approach goes a long way toward bridging the gap between traditional relevance feedback based only on assumed relationships in the image, and full human language queries. The graduate students involved in the project will be required to publish in international conferences and journals and will likely use this research as a basis for their dissertations. Other impacts of this work include the mentoring of graduate students and the inclusion of junior personnel in the management of the project. Research will be disseminated through local, national, and international meetings and journals. The team will also install a server and public interface for demonstration on existing datasets. The system will be accessible through the web on limited datasets and on the full dataset by request.