@inproceedings{a17674c645274bbea92ba81cfcf908b3,
title = "End-to-end pedestrian collision warning system based on a convolutional neural network with semantic segmentation",
abstract = "Traditional pedestrian collision warning systems sometimes raise alarms even when there is no danger (e.g., when all pedestrians are walking on the sidewalk). These false alarms can make it difficult for drivers to concentrate on their driving. In this paper, we propose a novel framework for an end-to-end pedestrian collision warning system based on a convolutional neural network. Semantic segmentation information is used to train the convolutional neural network and two loss functions, such as cross entropy and Euclidean losses, are minimized. Finally, we demonstrate the effectiveness of our method in reducing false alarms and increasing warning accuracy compared to a traditional histogram of oriented gradients (HOG)-based system.",
author = "Heechul Jung and Choi, {Min Kook} and Kwon Soon and Jung, {Woo Young}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Consumer Electronics, ICCE 2018 ; Conference date: 12-01-2018 Through 14-01-2018",
year = "2018",
month = mar,
day = "26",
doi = "10.1109/ICCE.2018.8326129",
language = "English",
series = "2018 IEEE International Conference on Consumer Electronics, ICCE 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--3",
editor = "Mohanty, {Saraju P.} and Peter Corcoran and Hai Li and Anirban Sengupta and Jong-Hyouk Lee",
booktitle = "2018 IEEE International Conference on Consumer Electronics, ICCE 2018",
address = "United States",
}