@inproceedings{4b2c525cfd0d459db4ede6a76387b310,
title = "Pedestrian detection using deep channel features in monocular image sequences",
abstract = "In this paper, we propose the Deep Channel Features as an extension to Channel Features for pedestrian detection. Instead of using hand-crafted features, our method automatically learns deep channel features as a mid-level feature by using a convolutional neural network. The network is pretrained by the unsupervised sparse filtering and a group of filters is learned for each channel. Combining the learned deep channel features with other low-level channel features (i.e. LUV channels, gradient magnitude channel and histogram of gradient channels) as the final feature, a boosting classifier with depth-2 decision tree as the weak classifier is learned. Our method achieves a significant detection performance on public datasets (i.e. INRIA, ETH, TUD, and CalTech).",
keywords = "Deep channel features, Deep learning, Mid-level features, Pedestrian detection",
author = "Zhao Liu and Yang He and Yi Xie and Hongyan Gu and Chao Liu and Mingtao Pei",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.",
year = "2016",
doi = "10.1007/978-3-319-46675-0\_67",
language = "English",
isbn = "9783319466743",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "608--615",
editor = "Akira Hirose and Minho Lee and Derong Liu and Kenji Doya and Kazushi Ikeda and Seiichi Ozawa",
booktitle = "Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings",
address = "Germany",
}