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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Pedestrian Detection Using Deep Channel Features in Monocular Image Sequences

verfasst von : Zhao Liu, Yang He, Yi Xie, Hongyan Gu, Chao Liu, Mingtao Pei

Erschienen in: Neural Information Processing

Verlag: Springer International Publishing

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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).

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Vorheriges Kapitel Stacked Robust Autoencoder for Classification
Nächstes Kapitel Heterogeneous Multi-task Learning on Non-overlapping Datasets for Facial Landmark Detection
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Metadaten
Titel
Pedestrian Detection Using Deep Channel Features in Monocular Image Sequences
verfasst von
Zhao Liu
Yang He
Yi Xie
Hongyan Gu
Chao Liu
Mingtao Pei
Copyright-Jahr
2016
Buch
Neural Information Processing

Print ISBN: 978-3-319-46674-3

Electronic ISBN: 978-3-319-46675-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46675-0_67