A Virtualized Video Surveillance System for Public Transportation
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.
Cite
Text
Marinc et al. "A Virtualized Video Surveillance System for Public Transportation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. doi:10.1007/978-3-030-46133-1_50Markdown
[Marinc et al. "A Virtualized Video Surveillance System for Public Transportation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.](https://mlanthology.org/ecmlpkdd/2019/marinc2019ecmlpkdd-virtualized/) doi:10.1007/978-3-030-46133-1_50BibTeX
@inproceedings{marinc2019ecmlpkdd-virtualized,
title = {{A Virtualized Video Surveillance System for Public Transportation}},
author = {Marinc, Talmaj and Gül, Serhan and Hellge, Cornelius and Schüßler, Peter and Riegel, Thomas and Amon, Peter},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2019},
pages = {777-780},
doi = {10.1007/978-3-030-46133-1_50},
url = {https://mlanthology.org/ecmlpkdd/2019/marinc2019ecmlpkdd-virtualized/}
}