Visual Recognition to Access and Analyze People Density and Flow Patterns in Indoor Environments
Abstract
This work describes our experience developing a system to access density and flow of people in large indoor spaces using a network of RGB cameras. The proposed system is based on a set of overlapped and calibrated cameras. This facilitates the use of geometric constraints that help to reduce visual ambiguities. These constraints are combined with classifiers based on visual appearance to produce an efficient and robust method to detect and track humans. In this work, we argue that flow and density of people are low level measurements that need to be complemented with suitable analytic tools to bridge semantic gaps and become useful information for a target application. Consequently, we also propose a set of analytic tools that help a human user to effectively take advantage of the measurements provided by the system. Finally, we report results that demonstrate the relevance of the proposed ideas.
Cite
Text
Ruz et al. "Visual Recognition to Access and Analyze People Density and Flow Patterns in Indoor Environments." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015. doi:10.1109/WACV.2015.8Markdown
[Ruz et al. "Visual Recognition to Access and Analyze People Density and Flow Patterns in Indoor Environments." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015.](https://mlanthology.org/wacv/2015/ruz2015wacv-visual/) doi:10.1109/WACV.2015.8BibTeX
@inproceedings{ruz2015wacv-visual,
title = {{Visual Recognition to Access and Analyze People Density and Flow Patterns in Indoor Environments}},
author = {Ruz, Cristian and Pieringer, Christian and Peralta, Billy and Lillo, Ivan and Espinace, Pablo and Gonzalez, R. and Wendt, B. and Mery, Domingo and Soto, Alvaro},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2015},
pages = {1-8},
doi = {10.1109/WACV.2015.8},
url = {https://mlanthology.org/wacv/2015/ruz2015wacv-visual/}
}