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Automatic Audio and Video Event Recognition in an Intelligent Resource Management System

Automatic Audio and Video Event Recognition in an Intelligent Resource Management System

Daniel Stein, Barbara Krausz, Jobst Löffler, Robin Marterer, Rolf Bardeli, Michael Stadtschnitzer, Jochen Schwenninger
Copyright: © 2013 |Volume: 5 |Issue: 4 |Pages: 12
ISSN: 1937-9390|EISSN: 1937-9420|EISBN13: 9781466635319|DOI: 10.4018/ijiscram.2013100101
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MLA

Stein, Daniel, et al. "Automatic Audio and Video Event Recognition in an Intelligent Resource Management System." IJISCRAM vol.5, no.4 2013: pp.1-12. https://doi.org/10.4018/ijiscram.2013100101

APA

Stein, D., Krausz, B., Löffler, J., Marterer, R., Bardeli, R., Stadtschnitzer, M., & Schwenninger, J. (2013). Automatic Audio and Video Event Recognition in an Intelligent Resource Management System. International Journal of Information Systems for Crisis Response and Management (IJISCRAM), 5(4), 1-12. https://doi.org/10.4018/ijiscram.2013100101

Chicago

Stein, Daniel, et al. "Automatic Audio and Video Event Recognition in an Intelligent Resource Management System," International Journal of Information Systems for Crisis Response and Management (IJISCRAM) 5, no.4: 1-12. https://doi.org/10.4018/ijiscram.2013100101

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Abstract

Event recognition systems have high potential to support crisis management and emergency response. For large-scale scenarios, however, the sheer amount of possible audio and video channels requires adequate processing of the material by automatic means. In this article, the authors focus on automatic audio and video event recognition, by means of detecting abnormalities both in train noise as well as surveillance videos, and by conducting automatic speech recognition on fire fighter communication. All components are integrated in an overall intelligent resource management system. The authors elaborate on the challenges expected from real life data and the solutions that the authors applied. The overall system, based on Event-Driven Service-Oriented Architecture, has been implemented and partly integrated into the end users' infrastructures. The system has been continuously running for more than two years, collecting data for research purposes.

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