Data-Driven Motion Pattern Segmentation in a Crowded Environments
Abstract
Motion is a strong clue for unsupervised grouping of individuals in a crowded environment. We show that collective motion in the crowd can be discovered by temporal analysis of points trajectories. First k-NN graph is constructed to represent the topological structure of point trajectories detected in crowd. Then the data-driven graph segmentation and clustering helps to reveal the interaction of individuals even when mixed motion is presented in data. The method was evaluated against the latest state-of-the-art methods and achieved better performance by more than 20 %.
Cite
Text
Trojanová et al. "Data-Driven Motion Pattern Segmentation in a Crowded Environments." European Conference on Computer Vision Workshops, 2016. doi:10.1007/978-3-319-48881-3_53Markdown
[Trojanová et al. "Data-Driven Motion Pattern Segmentation in a Crowded Environments." European Conference on Computer Vision Workshops, 2016.](https://mlanthology.org/eccvw/2016/trojanova2016eccvw-datadriven/) doi:10.1007/978-3-319-48881-3_53BibTeX
@inproceedings{trojanova2016eccvw-datadriven,
title = {{Data-Driven Motion Pattern Segmentation in a Crowded Environments}},
author = {Trojanová, Jana and Krehnác, Karel and Brémond, François},
booktitle = {European Conference on Computer Vision Workshops},
year = {2016},
pages = {760-774},
doi = {10.1007/978-3-319-48881-3_53},
url = {https://mlanthology.org/eccvw/2016/trojanova2016eccvw-datadriven/}
}