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2018 | OriginalPaper | Buchkapitel

An Effective Head Detection Framework via Convolutional Neural Networks

verfasst von : Canmiao Fu, Yule Yuan, Qiang Zeng, Siying He, Yong Zhao

Erschienen in: Advances in Multimedia Information Processing – PCM 2017

Verlag: Springer International Publishing

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Abstract

In this paper, we propose a conceptually simple, advanced and effective head detection framework based on convolutional network. To robustly detect the smaller size of the head in crowded scenes, we propose a new feature extraction strategy which uses a top-down structure and uses lateral connection to combine hierarchical features. Moreover, multi-scale RPN and weight sensitive layer are also explored without increase in the computation costs, as that can reinforce feature representation which is important for identifying small objects. Furthermore, in order to adapt to the needs of the actual application scenarios, we design a model whose size is reduced from 520 M to only 12 M and modify the classification network, which perfect realization of the low calculation and light-weight. We validated our approach on the Brainwash dataset where we show an admirable result compare to the state-of-the-art head detection.

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Titel: An Effective Head Detection Framework via Convolutional Neural Networks
verfasst von: Canmiao Fu
Yule Yuan
Qiang Zeng
Siying He
Yong Zhao
Verlag: Springer International Publishing
Buch: Advances in Multimedia Information Processing – PCM 2017
Print ISBN: 978-3-319-77382-7

Electronic ISBN: 978-3-319-77383-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-77383-4_47

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