Street Scene: A New Dataset and Evaluation Protocol for Video Anomaly Detection
Abstract
Progress in video anomaly detection research is currently slowed by small datasets that lack a wide variety of activities as well as flawed evaluation criteria. This paper aims to help move this research effort forward by introducing a large and varied new dataset called Street Scene, as well as two new evaluation criteria that provide a better estimate of how an algorithm will perform in practice. In addition to the new dataset and evaluation criteria, we present two variations of a novel baseline video anomaly detection algorithm and show they are much more accurate on Street Scene than two well known algorithms from the literature.
Cite
Text
Ramachandra and Jones. "Street Scene: A New Dataset and Evaluation Protocol for Video Anomaly Detection." Winter Conference on Applications of Computer Vision, 2020.Markdown
[Ramachandra and Jones. "Street Scene: A New Dataset and Evaluation Protocol for Video Anomaly Detection." Winter Conference on Applications of Computer Vision, 2020.](https://mlanthology.org/wacv/2020/ramachandra2020wacv-street/)BibTeX
@inproceedings{ramachandra2020wacv-street,
title = {{Street Scene: A New Dataset and Evaluation Protocol for Video Anomaly Detection}},
author = {Ramachandra, Bharathkumar and Jones, Michael},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2020},
url = {https://mlanthology.org/wacv/2020/ramachandra2020wacv-street/}
}