Abstract
Modern surveillance systems have recently started to employ computer vision algorithms for advanced analysis of the captured video content. Public transportation is one of the domains that may highly benefit from the advances in video analysis. This paper presents a video-based surveillance system that uses a deep neural network based face verification algorithm to accurately and robustly re-identify a subject person. Our implementation is highly scalable due to its container-based architecture and is easily deployable on a cloud platform to support larger processing loads. During the demo, the users will be able to interactively select a target person from pre-recorded surveillance videos and inspect the results on our web-based visualization platform.
This research has received funding from the German Federal Ministry for Economic Affairs and Energy under the VIRTUOSE-DE project.
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Marinč, T., Gül, S., Hellge, C., Schüßler, P., Riegel, T., Amon, P. (2020). A Virtualized Video Surveillance System for Public Transportation. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11908. Springer, Cham. https://doi.org/10.1007/978-3-030-46133-1_50
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DOI: https://doi.org/10.1007/978-3-030-46133-1_50
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