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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection

verfasst von : Bei Tong, Bin Fan, Fuchao Wu

Erschienen in: Pattern Recognition

Verlag: Springer Singapore

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Abstract

The combination of traditional methods (e.g., ACF) and Convolutional Neural Networks (CNNs) has achieved great success in pedestrian detection. Despite effectiveness, design of this method is intricate. In this paper, we present an end-to-end network based on Faster R-CNN and neural cascade classifier for pedestrian detection. Different from Faster R-CNN that only makes use of the last convolutional layer, we utilize features from multiple layers and feed them to a neural cascade classifier. Such an architecture favors more low-level features and implements a hard negative mining process in the network. Both of these two factors are important in pedestrian detection. The neural cascade classifier is jointly trained with the Faster R-CNN in our unifying network. The proposed network achieves comparable performance to the state-of-the-art on Caltech pedestrian dataset with a more concise framework and faster processing speed. Meanwhile, the detection result obtained by our method is tighter and more accurate.

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Vorheriges Kapitel Research on the Stability of Plantar Pressure Under Normal Walking Condition
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Metadaten
Titel
Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection
verfasst von
Bei Tong
Bin Fan
Fuchao Wu
Copyright-Jahr
2016
Verlag
Springer Singapore
Buch
Pattern Recognition

Print ISBN: 978-981-10-3001-7

Electronic ISBN: 978-981-10-3002-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-981-10-3002-4_21

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