Abstract
Automated visual surveillance has emerged as a trendy application domain in recent years. Many approaches have been developed on video processing and understanding. Content-based access to surveillance video has become a challenging research area. The results of a considerable amount of work dealing with automated access to visual surveillance have appeared in the literature. However, the event models and the content-based querying and retrieval components have significant gaps remaining unfilled. To narrow these gaps, we propose a database model for querying surveillance videos by integrating semantic and low-level features. In this paper, the initial design of the database model, the query types, and the specifications of its query language are presented.
This work is supported in part by Turkish State Planning Organization (DPT) under grant number 2004K120720, and European Commission 6th Framework Program MUSCLE Network of Excellence Project with grant number FP6-507752.
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Şaykol, E., Güdükbay, U., Ulusoy, Ö. (2005). A Database Model for Querying Visual Surveillance Videos by Integrating Semantic and Low-Level Features. In: Candan, K.S., Celentano, A. (eds) Advances in Multimedia Information Systems. MIS 2005. Lecture Notes in Computer Science, vol 3665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551898_15
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DOI: https://doi.org/10.1007/11551898_15
Publisher Name: Springer, Berlin, Heidelberg
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