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A semantic-guided and self-configurable framework for video analysis

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Abstract

This paper presents a distributed and scalable framework for video analysis that automatically estimates the optimal workflow required for the analysis of different application domains. It integrates several technologies related with data acquisition, visual analysis tools, communication protocols, and data storage. Moreover, hierarchical semantic representations are included in the framework to describe the application domain, the analysis capabilities, and the user preferences. The automatic determination of the analysis workflow is performed by selecting the most appropriate tools for each domain among the available ones in the framework by means of exploiting the relations between the semantic descriptions. The experimental results in the video surveillance domain demonstrate that the proposed approach successfully composes optimal workflows for video analysis applications.

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Authors and Affiliations

  1. Video Processing and Understanding Lab (VPULab), Department of Tecnología Electrónica y de las Comunicaciones, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain

    Juan C. SanMiguel & José M. Martínez

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  1. Juan C. SanMiguel
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  2. José M. Martínez
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Correspondence to Juan C. SanMiguel.

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SanMiguel, J.C., Martínez, J.M. A semantic-guided and self-configurable framework for video analysis. Machine Vision and Applications 24, 493–512 (2013). https://doi.org/10.1007/s00138-011-0397-x

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  • DOI: https://doi.org/10.1007/s00138-011-0397-x

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