Abnormal Crowd Behavior Detection Based on Gaussian Mixture Model
Abstract
Many of the state-of-the-art approaches for automatic abnormal behavior detection in crowded scenes are based on complex models which require high processing time and several parameters to be adjusted. This paper presents a simple new approach that uses background subtraction algorithm and optical flow to encode the normal behavior pattern through a Gaussian Mixture Model (GMM). Abnormal behavior is detected comparing new samples against the mixture model. Experimental results on standards anomaly detection and localization benchmarks are presented and compared to other algorithms considering detection rate and processing time.
Cite
Text
Rojas and Tozzi. "Abnormal Crowd Behavior Detection Based on Gaussian Mixture Model." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-48881-3_47Markdown
[Rojas and Tozzi. "Abnormal Crowd Behavior Detection Based on Gaussian Mixture Model." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/rojas2016eccv-abnormal/) doi:10.1007/978-3-319-48881-3_47BibTeX
@inproceedings{rojas2016eccv-abnormal,
title = {{Abnormal Crowd Behavior Detection Based on Gaussian Mixture Model}},
author = {Rojas, Oscar Ernesto and Tozzi, Clésio Luis},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {668-675},
doi = {10.1007/978-3-319-48881-3_47},
url = {https://mlanthology.org/eccv/2016/rojas2016eccv-abnormal/}
}