Boosted Convolutional Neural Networks (BCNN) for Pedestrian Detection
Abstract
A boosted convolutional neural network (BCNN) system is proposed to enhance the pedestrian detection performance in this work. Being inspired by the classic boosting idea, we develop a weighted loss function that emphasizes challenging samples in training a convolutional neural network (CNN). Two types of samples are considered challenging: 1) samples with detection scores falling in the decision boundary, and 2) temporally associated samples with inconsistent scores. A weighting scheme is designed for each of them. Finally, we train a boosted fusion layer to benefit from the integration of these two weighting schemes. We use the Fast-RCNN as the baseline, and test the corresponding BCNN on the Caltech pedestrian dataset in the experiment, and show a significant performance gain of the BCNN over its baseline.
Cite
Text
Wu et al. "Boosted Convolutional Neural Networks (BCNN) for Pedestrian Detection." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017. doi:10.1109/WACV.2017.66Markdown
[Wu et al. "Boosted Convolutional Neural Networks (BCNN) for Pedestrian Detection." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017.](https://mlanthology.org/wacv/2017/wu2017wacv-boosted/) doi:10.1109/WACV.2017.66BibTeX
@inproceedings{wu2017wacv-boosted,
title = {{Boosted Convolutional Neural Networks (BCNN) for Pedestrian Detection}},
author = {Wu, Chi-Hao and Gan, Weihao and Lan, De and Kuo, C.-C. Jay},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2017},
pages = {540-549},
doi = {10.1109/WACV.2017.66},
url = {https://mlanthology.org/wacv/2017/wu2017wacv-boosted/}
}