CYKLS: Detect Pedestrian's Dart Focusing on an Appearance Change
Abstract
We propose a new method for detecting “pedestrians’ dart” to support drivers cognition in real traffic scenario. The main idea is to detect sudden appearance change of pedestrians before their consequent actions happen. Our new algorithm, called “Chronologically Yielded values of Kullback-Leibler divergence between Separate frames” (CYKLS), is a combination of two main procedures: (1) calculation of appearance change by Kullback-Leibler divergence between descriptors in some time interval frames, and (2) detection of non-periodic sequence by a new smoothing method in the field of time series analysis. We can detect pedestrians’ dart with 22% Equal Error Rate, using a dataset which includes 144 dart scenes.
Cite
Text
Ogawa et al. "CYKLS: Detect Pedestrian's Dart Focusing on an Appearance Change." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33868-7_55Markdown
[Ogawa et al. "CYKLS: Detect Pedestrian's Dart Focusing on an Appearance Change." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/ogawa2012eccv-cykls/) doi:10.1007/978-3-642-33868-7_55BibTeX
@inproceedings{ogawa2012eccv-cykls,
title = {{CYKLS: Detect Pedestrian's Dart Focusing on an Appearance Change}},
author = {Ogawa, Masahiro and Fukamachi, Hideo and Funayama, Ryuji and Kindo, Toshiki},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {556-565},
doi = {10.1007/978-3-642-33868-7_55},
url = {https://mlanthology.org/eccv/2012/ogawa2012eccv-cykls/}
}