A Scalable System Architecture for Activity Detection with Simple Heuristics
Abstract
The analysis of video footage regarding the identification of persons at defined locations or the detection of complex activities is still a challenging process. Nowadays, various (semi-)automated systems can be used to overcome different parts of these challenges. Object detection and their classification reach even higher detection rates when making use of the latest cutting-edge convolutional neural network frameworks. Integrated into a scalable infrastructure as a service data base system, we employ the combination of such networks by using the Detectron framework within Docker containers with case-specific engineered tracking and motion pattern heuristics in order to detect several activities with comparatively low and distributed computing efforts and reasonable results.
Cite
Text
Thomanek et al. "A Scalable System Architecture for Activity Detection with Simple Heuristics." IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2019. doi:10.1109/WACVW.2019.00012Markdown
[Thomanek et al. "A Scalable System Architecture for Activity Detection with Simple Heuristics." IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2019.](https://mlanthology.org/wacvw/2019/thomanek2019wacvw-scalable/) doi:10.1109/WACVW.2019.00012BibTeX
@inproceedings{thomanek2019wacvw-scalable,
title = {{A Scalable System Architecture for Activity Detection with Simple Heuristics}},
author = {Thomanek, Rico and Roschke, Christian and Platte, Benny and Manthey, Robert and Rolletschke, Tony and Heinzig, Manuel and Vodel, Matthias and Zimmer, Frank and Eibl, Maximilian and Ritter, Marc},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision Workshops},
year = {2019},
pages = {27-34},
doi = {10.1109/WACVW.2019.00012},
url = {https://mlanthology.org/wacvw/2019/thomanek2019wacvw-scalable/}
}